Spam Notebook
Business Understanding
Spam can lead to some bad stuff, let’s stop it.
Load libraries and packages
library(e1071) #library for skewness
require(xgboost)
library(pROC)
library(magrittr)
library(dplyr)Load data
rm(list=ls())
spam<-read.csv("/Users/kent/Library/Mobile Documents/com~apple~CloudDocs/Leeds/Spring/MSBX5415-002AdvancedDataAnalytics/Project/dataset_44_spambase.csv",header=T,na.strings=c("","NA"))Quick look at top of the file
head(spam)Quick look at the bottom of the file
tail(spam)How big is file
dim(spam)[1] 4601 58Data Understanding
This data is a curated sample from HP, should not be considered a random sample, each row is an email word_freq… = frequency of word in email char_freq… = " character capital… = stats on capitalized… class = label, target, binary
Look at content, quick summary
str(spam)'data.frame': 4601 obs. of 58 variables:
$ word_freq_make : num 0 0.21 0.06 0 0 0 0 0 0.15 0.06 ...
$ word_freq_address : num 0.64 0.28 0 0 0 0 0 0 0 0.12 ...
$ word_freq_all : num 0.64 0.5 0.71 0 0 0 0 0 0.46 0.77 ...
$ word_freq_3d : num 0 0 0 0 0 0 0 0 0 0 ...
$ word_freq_our : num 0.32 0.14 1.23 0.63 0.63 1.85 1.92 1.88 0.61 0.19 ...
$ word_freq_over : num 0 0.28 0.19 0 0 0 0 0 0 0.32 ...
$ word_freq_remove : num 0 0.21 0.19 0.31 0.31 0 0 0 0.3 0.38 ...
$ word_freq_internet : num 0 0.07 0.12 0.63 0.63 1.85 0 1.88 0 0 ...
$ word_freq_order : num 0 0 0.64 0.31 0.31 0 0 0 0.92 0.06 ...
$ word_freq_mail : num 0 0.94 0.25 0.63 0.63 0 0.64 0 0.76 0 ...
$ word_freq_receive : num 0 0.21 0.38 0.31 0.31 0 0.96 0 0.76 0 ...
$ word_freq_will : num 0.64 0.79 0.45 0.31 0.31 0 1.28 0 0.92 0.64 ...
$ word_freq_people : num 0 0.65 0.12 0.31 0.31 0 0 0 0 0.25 ...
$ word_freq_report : num 0 0.21 0 0 0 0 0 0 0 0 ...
$ word_freq_addresses : num 0 0.14 1.75 0 0 0 0 0 0 0.12 ...
$ word_freq_free : num 0.32 0.14 0.06 0.31 0.31 0 0.96 0 0 0 ...
$ word_freq_business : num 0 0.07 0.06 0 0 0 0 0 0 0 ...
$ word_freq_email : num 1.29 0.28 1.03 0 0 0 0.32 0 0.15 0.12 ...
$ word_freq_you : num 1.93 3.47 1.36 3.18 3.18 0 3.85 0 1.23 1.67 ...
$ word_freq_credit : num 0 0 0.32 0 0 0 0 0 3.53 0.06 ...
$ word_freq_your : num 0.96 1.59 0.51 0.31 0.31 0 0.64 0 2 0.71 ...
$ word_freq_font : num 0 0 0 0 0 0 0 0 0 0 ...
$ word_freq_000 : num 0 0.43 1.16 0 0 0 0 0 0 0.19 ...
$ word_freq_money : num 0 0.43 0.06 0 0 0 0 0 0.15 0 ...
$ word_freq_hp : num 0 0 0 0 0 0 0 0 0 0 ...
$ word_freq_hpl : num 0 0 0 0 0 0 0 0 0 0 ...
$ word_freq_george : num 0 0 0 0 0 0 0 0 0 0 ...
$ word_freq_650 : num 0 0 0 0 0 0 0 0 0 0 ...
$ word_freq_lab : num 0 0 0 0 0 0 0 0 0 0 ...
$ word_freq_labs : num 0 0 0 0 0 0 0 0 0 0 ...
$ word_freq_telnet : num 0 0 0 0 0 0 0 0 0 0 ...
$ word_freq_857 : num 0 0 0 0 0 0 0 0 0 0 ...
$ word_freq_data : num 0 0 0 0 0 0 0 0 0.15 0 ...
$ word_freq_415 : num 0 0 0 0 0 0 0 0 0 0 ...
$ word_freq_85 : num 0 0 0 0 0 0 0 0 0 0 ...
$ word_freq_technology : num 0 0 0 0 0 0 0 0 0 0 ...
$ word_freq_1999 : num 0 0.07 0 0 0 0 0 0 0 0 ...
$ word_freq_parts : num 0 0 0 0 0 0 0 0 0 0 ...
$ word_freq_pm : num 0 0 0 0 0 0 0 0 0 0 ...
$ word_freq_direct : num 0 0 0.06 0 0 0 0 0 0 0 ...
$ word_freq_cs : num 0 0 0 0 0 0 0 0 0 0 ...
$ word_freq_meeting : num 0 0 0 0 0 0 0 0 0 0 ...
$ word_freq_original : num 0 0 0.12 0 0 0 0 0 0.3 0 ...
$ word_freq_project : num 0 0 0 0 0 0 0 0 0 0.06 ...
$ word_freq_re : num 0 0 0.06 0 0 0 0 0 0 0 ...
$ word_freq_edu : num 0 0 0.06 0 0 0 0 0 0 0 ...
$ word_freq_table : num 0 0 0 0 0 0 0 0 0 0 ...
$ word_freq_conference : num 0 0 0 0 0 0 0 0 0 0 ...
$ char_freq_.3B : num 0 0 0.01 0 0 0 0 0 0 0.04 ...
$ char_freq_.28 : num 0 0.132 0.143 0.137 0.135 0.223 0.054 0.206 0.271 0.03 ...
$ char_freq_.5B : num 0 0 0 0 0 0 0 0 0 0 ...
$ char_freq_.21 : num 0.778 0.372 0.276 0.137 0.135 0 0.164 0 0.181 0.244 ...
$ char_freq_.24 : num 0 0.18 0.184 0 0 0 0.054 0 0.203 0.081 ...
$ char_freq_.23 : num 0 0.048 0.01 0 0 0 0 0 0.022 0 ...
$ capital_run_length_average: num 3.76 5.11 9.82 3.54 3.54 ...
$ capital_run_length_longest: int 61 101 485 40 40 15 4 11 445 43 ...
$ capital_run_length_total : int 278 1028 2259 191 191 54 112 49 1257 749 ...
$ class : int 1 1 1 1 1 1 1 1 1 1 ...Descriptive stats
summary(spam) word_freq_make word_freq_address word_freq_all word_freq_3d
Min. :0.0000 Min. : 0.000 Min. :0.0000 Min. : 0.00000
1st Qu.:0.0000 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.: 0.00000
Median :0.0000 Median : 0.000 Median :0.0000 Median : 0.00000
Mean :0.1046 Mean : 0.213 Mean :0.2807 Mean : 0.06542
3rd Qu.:0.0000 3rd Qu.: 0.000 3rd Qu.:0.4200 3rd Qu.: 0.00000
Max. :4.5400 Max. :14.280 Max. :5.1000 Max. :42.81000
word_freq_our word_freq_over word_freq_remove word_freq_internet
Min. : 0.0000 Min. :0.0000 Min. :0.0000 Min. : 0.0000
1st Qu.: 0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 0.0000
Median : 0.0000 Median :0.0000 Median :0.0000 Median : 0.0000
Mean : 0.3122 Mean :0.0959 Mean :0.1142 Mean : 0.1053
3rd Qu.: 0.3800 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.: 0.0000
Max. :10.0000 Max. :5.8800 Max. :7.2700 Max. :11.1100
word_freq_order word_freq_mail word_freq_receive word_freq_will
Min. :0.00000 Min. : 0.0000 Min. :0.00000 Min. :0.0000
1st Qu.:0.00000 1st Qu.: 0.0000 1st Qu.:0.00000 1st Qu.:0.0000
Median :0.00000 Median : 0.0000 Median :0.00000 Median :0.1000
Mean :0.09007 Mean : 0.2394 Mean :0.05982 Mean :0.5417
3rd Qu.:0.00000 3rd Qu.: 0.1600 3rd Qu.:0.00000 3rd Qu.:0.8000
Max. :5.26000 Max. :18.1800 Max. :2.61000 Max. :9.6700
word_freq_people word_freq_report word_freq_addresses word_freq_free
Min. :0.00000 Min. : 0.00000 Min. :0.0000 Min. : 0.0000
1st Qu.:0.00000 1st Qu.: 0.00000 1st Qu.:0.0000 1st Qu.: 0.0000
Median :0.00000 Median : 0.00000 Median :0.0000 Median : 0.0000
Mean :0.09393 Mean : 0.05863 Mean :0.0492 Mean : 0.2488
3rd Qu.:0.00000 3rd Qu.: 0.00000 3rd Qu.:0.0000 3rd Qu.: 0.1000
Max. :5.55000 Max. :10.00000 Max. :4.4100 Max. :20.0000
word_freq_business word_freq_email word_freq_you word_freq_credit
Min. :0.0000 Min. :0.0000 Min. : 0.000 Min. : 0.00000
1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 0.000 1st Qu.: 0.00000
Median :0.0000 Median :0.0000 Median : 1.310 Median : 0.00000
Mean :0.1426 Mean :0.1847 Mean : 1.662 Mean : 0.08558
3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.: 2.640 3rd Qu.: 0.00000
Max. :7.1400 Max. :9.0900 Max. :18.750 Max. :18.18000
word_freq_your word_freq_font word_freq_000 word_freq_money
Min. : 0.0000 Min. : 0.0000 Min. :0.0000 Min. : 0.00000
1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.:0.0000 1st Qu.: 0.00000
Median : 0.2200 Median : 0.0000 Median :0.0000 Median : 0.00000
Mean : 0.8098 Mean : 0.1212 Mean :0.1016 Mean : 0.09427
3rd Qu.: 1.2700 3rd Qu.: 0.0000 3rd Qu.:0.0000 3rd Qu.: 0.00000
Max. :11.1100 Max. :17.1000 Max. :5.4500 Max. :12.50000
word_freq_hp word_freq_hpl word_freq_george word_freq_650
Min. : 0.0000 Min. : 0.0000 Min. : 0.0000 Min. :0.0000
1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.:0.0000
Median : 0.0000 Median : 0.0000 Median : 0.0000 Median :0.0000
Mean : 0.5495 Mean : 0.2654 Mean : 0.7673 Mean :0.1248
3rd Qu.: 0.0000 3rd Qu.: 0.0000 3rd Qu.: 0.0000 3rd Qu.:0.0000
Max. :20.8300 Max. :16.6600 Max. :33.3300 Max. :9.0900
word_freq_lab word_freq_labs word_freq_telnet word_freq_857
Min. : 0.00000 Min. :0.0000 Min. : 0.00000 Min. :0.00000
1st Qu.: 0.00000 1st Qu.:0.0000 1st Qu.: 0.00000 1st Qu.:0.00000
Median : 0.00000 Median :0.0000 Median : 0.00000 Median :0.00000
Mean : 0.09892 Mean :0.1029 Mean : 0.06475 Mean :0.04705
3rd Qu.: 0.00000 3rd Qu.:0.0000 3rd Qu.: 0.00000 3rd Qu.:0.00000
Max. :14.28000 Max. :5.8800 Max. :12.50000 Max. :4.76000
word_freq_data word_freq_415 word_freq_85 word_freq_technology
Min. : 0.00000 Min. :0.00000 Min. : 0.0000 Min. :0.00000
1st Qu.: 0.00000 1st Qu.:0.00000 1st Qu.: 0.0000 1st Qu.:0.00000
Median : 0.00000 Median :0.00000 Median : 0.0000 Median :0.00000
Mean : 0.09723 Mean :0.04784 Mean : 0.1054 Mean :0.09748
3rd Qu.: 0.00000 3rd Qu.:0.00000 3rd Qu.: 0.0000 3rd Qu.:0.00000
Max. :18.18000 Max. :4.76000 Max. :20.0000 Max. :7.69000
word_freq_1999 word_freq_parts word_freq_pm word_freq_direct
Min. :0.000 Min. :0.0000 Min. : 0.00000 Min. :0.00000
1st Qu.:0.000 1st Qu.:0.0000 1st Qu.: 0.00000 1st Qu.:0.00000
Median :0.000 Median :0.0000 Median : 0.00000 Median :0.00000
Mean :0.137 Mean :0.0132 Mean : 0.07863 Mean :0.06483
3rd Qu.:0.000 3rd Qu.:0.0000 3rd Qu.: 0.00000 3rd Qu.:0.00000
Max. :6.890 Max. :8.3300 Max. :11.11000 Max. :4.76000
word_freq_cs word_freq_meeting word_freq_original word_freq_project
Min. :0.00000 Min. : 0.0000 Min. :0.0000 Min. : 0.0000
1st Qu.:0.00000 1st Qu.: 0.0000 1st Qu.:0.0000 1st Qu.: 0.0000
Median :0.00000 Median : 0.0000 Median :0.0000 Median : 0.0000
Mean :0.04367 Mean : 0.1323 Mean :0.0461 Mean : 0.0792
3rd Qu.:0.00000 3rd Qu.: 0.0000 3rd Qu.:0.0000 3rd Qu.: 0.0000
Max. :7.14000 Max. :14.2800 Max. :3.5700 Max. :20.0000
word_freq_re word_freq_edu word_freq_table word_freq_conference
Min. : 0.0000 Min. : 0.0000 Min. :0.000000 Min. : 0.00000
1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.:0.000000 1st Qu.: 0.00000
Median : 0.0000 Median : 0.0000 Median :0.000000 Median : 0.00000
Mean : 0.3012 Mean : 0.1798 Mean :0.005444 Mean : 0.03187
3rd Qu.: 0.1100 3rd Qu.: 0.0000 3rd Qu.:0.000000 3rd Qu.: 0.00000
Max. :21.4200 Max. :22.0500 Max. :2.170000 Max. :10.00000
char_freq_.3B char_freq_.28 char_freq_.5B char_freq_.21
Min. :0.00000 Min. :0.000 Min. :0.00000 Min. : 0.0000
1st Qu.:0.00000 1st Qu.:0.000 1st Qu.:0.00000 1st Qu.: 0.0000
Median :0.00000 Median :0.065 Median :0.00000 Median : 0.0000
Mean :0.03857 Mean :0.139 Mean :0.01698 Mean : 0.2691
3rd Qu.:0.00000 3rd Qu.:0.188 3rd Qu.:0.00000 3rd Qu.: 0.3150
Max. :4.38500 Max. :9.752 Max. :4.08100 Max. :32.4780
char_freq_.24 char_freq_.23 capital_run_length_average
Min. :0.00000 Min. : 0.00000 Min. : 1.000
1st Qu.:0.00000 1st Qu.: 0.00000 1st Qu.: 1.588
Median :0.00000 Median : 0.00000 Median : 2.276
Mean :0.07581 Mean : 0.04424 Mean : 5.191
3rd Qu.:0.05200 3rd Qu.: 0.00000 3rd Qu.: 3.706
Max. :6.00300 Max. :19.82900 Max. :1102.500
capital_run_length_longest capital_run_length_total class
Min. : 1.00 Min. : 1.0 Min. :0.000
1st Qu.: 6.00 1st Qu.: 35.0 1st Qu.:0.000
Median : 15.00 Median : 95.0 Median :0.000
Mean : 52.17 Mean : 283.3 Mean :0.394
3rd Qu.: 43.00 3rd Qu.: 266.0 3rd Qu.:1.000
Max. :9989.00 Max. :15841.0 Max. :1.000 names<-names(spam)Look at column headers
print(names) [1] "word_freq_make" "word_freq_address"
[3] "word_freq_all" "word_freq_3d"
[5] "word_freq_our" "word_freq_over"
[7] "word_freq_remove" "word_freq_internet"
[9] "word_freq_order" "word_freq_mail"
[11] "word_freq_receive" "word_freq_will"
[13] "word_freq_people" "word_freq_report"
[15] "word_freq_addresses" "word_freq_free"
[17] "word_freq_business" "word_freq_email"
[19] "word_freq_you" "word_freq_credit"
[21] "word_freq_your" "word_freq_font"
[23] "word_freq_000" "word_freq_money"
[25] "word_freq_hp" "word_freq_hpl"
[27] "word_freq_george" "word_freq_650"
[29] "word_freq_lab" "word_freq_labs"
[31] "word_freq_telnet" "word_freq_857"
[33] "word_freq_data" "word_freq_415"
[35] "word_freq_85" "word_freq_technology"
[37] "word_freq_1999" "word_freq_parts"
[39] "word_freq_pm" "word_freq_direct"
[41] "word_freq_cs" "word_freq_meeting"
[43] "word_freq_original" "word_freq_project"
[45] "word_freq_re" "word_freq_edu"
[47] "word_freq_table" "word_freq_conference"
[49] "char_freq_.3B" "char_freq_.28"
[51] "char_freq_.5B" "char_freq_.21"
[53] "char_freq_.24" "char_freq_.23"
[55] "capital_run_length_average" "capital_run_length_longest"
[57] "capital_run_length_total" "class" column_names<-colnames(spam)Look at target, labeled 1 as spam, 0 as not spam
as.factor(spam$class) [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[41] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[81] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[121] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[161] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[201] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[241] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[281] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[321] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[361] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[401] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[441] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[481] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[521] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[561] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[601] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[641] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[681] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[721] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[761] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[801] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[841] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[881] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[921] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[961] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[ reached getOption("max.print") -- omitted 3601 entries ]
Levels: 0 1Look at amount of zero’s, data seems to be super clean and ready to roll, 0’s are legitimate since not every email has dictionary in it, content wise
colSums(is.na(spam)) word_freq_make word_freq_address word_freq_all
0 0 0
word_freq_3d word_freq_our word_freq_over
0 0 0
word_freq_remove word_freq_internet word_freq_order
0 0 0
word_freq_mail word_freq_receive word_freq_will
0 0 0
word_freq_people word_freq_report word_freq_addresses
0 0 0
word_freq_free word_freq_business word_freq_email
0 0 0
word_freq_you word_freq_credit word_freq_your
0 0 0
word_freq_font word_freq_000 word_freq_money
0 0 0
word_freq_hp word_freq_hpl word_freq_george
0 0 0
word_freq_650 word_freq_lab word_freq_labs
0 0 0
word_freq_telnet word_freq_857 word_freq_data
0 0 0
word_freq_415 word_freq_85 word_freq_technology
0 0 0
word_freq_1999 word_freq_parts word_freq_pm
0 0 0
word_freq_direct word_freq_cs word_freq_meeting
0 0 0
word_freq_original word_freq_project word_freq_re
0 0 0
word_freq_edu word_freq_table word_freq_conference
0 0 0
char_freq_.3B char_freq_.28 char_freq_.5B
0 0 0
char_freq_.21 char_freq_.24 char_freq_.23
0 0 0
capital_run_length_average capital_run_length_longest capital_run_length_total
0 0 0
class
0 for (i in names(spam)){
print(c('Percentage of zeros in ',i,sum(spam[,i]==0)/nrow(spam)))}[1] "Percentage of zeros in " "word_freq_make" "0.771136709410998"
[1] "Percentage of zeros in " "word_freq_address" "0.80482503803521"
[1] "Percentage of zeros in " "word_freq_all" "0.589654422951532"
[1] "Percentage of zeros in " "word_freq_3d" "0.989784829384916"
[1] "Percentage of zeros in " "word_freq_our" "0.620082590741143"
[1] "Percentage of zeros in " "word_freq_over" "0.782873288415562"
[1] "Percentage of zeros in " "word_freq_remove" "0.824603347098457"
[1] "Percentage of zeros in " "word_freq_internet" "0.820908498152575"
[1] "Percentage of zeros in " "word_freq_order" "0.83199304499022"
[1] "Percentage of zeros in " "word_freq_mail" "0.717018039556618"
[1] "Percentage of zeros in " "word_freq_receive" "0.845903064551185"
[1] "Percentage of zeros in " "word_freq_will" "0.494675070636818"
[1] "Percentage of zeros in " "word_freq_people" "0.814822864594653"
[1] "Percentage of zeros in " "word_freq_report" "0.922408172136492"
[1] "Percentage of zeros in " "word_freq_addresses" "0.926972397304934"
[1] "Percentage of zeros in " "word_freq_free" "0.730276026950663"
[1] "Percentage of zeros in " "word_freq_business" "0.790697674418605"
[1] "Percentage of zeros in " "word_freq_email" "0.774396870245599"
[1] "Percentage of zeros in " "word_freq_you" "0.298630732449468"
[1] "Percentage of zeros in " "word_freq_credit" "0.907846120408607"
[1] "Percentage of zeros in " "word_freq_your" "0.47337535318409"
[1] "Percentage of zeros in " "word_freq_font" "0.974570745490111"
[1] "Percentage of zeros in " "word_freq_000" "0.852423386220387"
[1] "Percentage of zeros in " "word_freq_money" "0.840252119104542"
[1] "Percentage of zeros in " "word_freq_hp" "0.763094979352315"
[1] "Percentage of zeros in " "word_freq_hpl" "0.823733970875897"
[1] "Percentage of zeros in " "word_freq_george" "0.830471636600739"
[1] "Percentage of zeros in " "word_freq_650" "0.899369702238644"
[1] "Percentage of zeros in " "word_freq_lab" "0.919148011301891"
[1] "Percentage of zeros in " "word_freq_labs" "0.898065637904803"
[1] "Percentage of zeros in " "word_freq_telnet" "0.936318191697457"
[1] "Percentage of zeros in " "word_freq_857" "0.955444468593784"
[1] "Percentage of zeros in " "word_freq_data" "0.911975657465768"
[1] "Percentage of zeros in " "word_freq_415" "0.953271028037383"
[1] "Percentage of zeros in " "word_freq_85" "0.894588133014562"
[1] "Percentage of zeros in " "word_freq_technology" "0.869810910671593"
[1] "Percentage of zeros in " "word_freq_1999" "0.819821777874375"
[1] "Percentage of zeros in " "word_freq_parts" "0.981960443381873"
[1] "Percentage of zeros in " "word_freq_pm" "0.91653988263421"
[1] "Percentage of zeros in " "word_freq_direct" "0.901543142795045"
[1] "Percentage of zeros in " "word_freq_cs" "0.967833079765268"
[1] "Percentage of zeros in " "word_freq_meeting" "0.925885677026733"
[1] "Percentage of zeros in " "word_freq_original" "0.918495979134971"
[1] "Percentage of zeros in " "word_freq_project" "0.928928493805694"
[1] "Percentage of zeros in " "word_freq_re" "0.715061943055857"
[1] "Percentage of zeros in " "word_freq_edu" "0.88763312323408"
[1] "Percentage of zeros in " "word_freq_table" "0.986307324494675"
[1] "Percentage of zeros in " "word_freq_conference" "0.955879156705064"
[1] "Percentage of zeros in " "char_freq_.3B" "0.828298196044338"
[1] "Percentage of zeros in " "char_freq_.28" "0.409910888937188"
[1] "Percentage of zeros in " "char_freq_.5B" "0.885024994566399"
[1] "Percentage of zeros in " "char_freq_.21" "0.509237122364703"
[1] "Percentage of zeros in " "char_freq_.24" "0.695718322103891"
[1] "Percentage of zeros in " "char_freq_.23" "0.836991958269941"
[1] "Percentage of zeros in " "capital_run_length_average"
[3] "0"
[1] "Percentage of zeros in " "capital_run_length_longest"
[3] "0"
[1] "Percentage of zeros in " "capital_run_length_total" "0"
[1] "Percentage of zeros in " "class" "0.605955227124538" A little EDA
Look at skewness
for (i in names(spam)){
print(c('Skewness in ',i,skewness(spam[,i])))}[1] "Skewness in " "word_freq_make" "5.67193900067991"
[1] "Skewness in " "word_freq_address" "10.0802349003634"
[1] "Skewness in " "word_freq_all" "3.00728668476344"
[1] "Skewness in " "word_freq_3d" "26.2106456152026"
[1] "Skewness in " "word_freq_our" "4.74403128369989"
[1] "Skewness in " "word_freq_over" "5.95306917430583"
[1] "Skewness in " "word_freq_remove" "6.76116973235819"
[1] "Skewness in " "word_freq_internet" "9.7185075353426"
[1] "Skewness in " "word_freq_order" "5.22265988103807"
[1] "Skewness in " "word_freq_mail" "8.48227599748207"
[1] "Skewness in " "word_freq_receive" "5.50665781347663"
[1] "Skewness in " "word_freq_will" "2.86548424803136"
[1] "Skewness in " "word_freq_people" "6.95101364285517"
[1] "Skewness in " "word_freq_report" "11.7469821788241"
[1] "Skewness in " "word_freq_addresses" "6.96649613902047"
[1] "Skewness in " "word_freq_free" "10.7565768368784"
[1] "Skewness in " "word_freq_business" "5.68493345831199"
[1] "Skewness in " "word_freq_email" "5.41022429253872"
[1] "Skewness in " "word_freq_you" "1.59063659626038"
[1] "Skewness in " "word_freq_credit" "14.5930666681311"
[1] "Skewness in " "word_freq_your" "2.43393936389113"
[1] "Skewness in " "word_freq_font" "9.9689373609874"
[1] "Skewness in " "word_freq_000" "5.71005047239415"
[1] "Skewness in " "word_freq_money" "14.6774530311175"
[1] "Skewness in " "word_freq_hp" "5.71311641715765"
[1] "Skewness in " "word_freq_hpl" "6.34587179123107"
[1] "Skewness in " "word_freq_george" "5.74074824213199"
[1] "Skewness in " "word_freq_650" "6.602226884938"
[1] "Skewness in " "word_freq_lab" "11.3628189767488"
[1] "Skewness in " "word_freq_labs" "6.63168895636077"
[1] "Skewness in " "word_freq_telnet" "12.6608216746697"
[1] "Skewness in " "word_freq_857" "10.5423067580283"
[1] "Skewness in " "word_freq_data" "13.1814567423014"
[1] "Skewness in " "word_freq_415" "10.4683520282183"
[1] "Skewness in " "word_freq_85" "15.2208819232757"
[1] "Skewness in " "word_freq_technology" "7.66845872961491"
[1] "Skewness in " "word_freq_1999" "5.32002109393731"
[1] "Skewness in " "word_freq_parts" "28.2447897111829"
[1] "Skewness in " "word_freq_pm" "12.0490516437972"
[1] "Skewness in " "word_freq_direct" "9.14106611591679"
[1] "Skewness in " "word_freq_cs" "12.5796938412588"
[1] "Skewness in " "word_freq_meeting" "9.44959035070889"
[1] "Skewness in " "word_freq_original" "7.62425421979753"
[1] "Skewness in " "word_freq_project" "18.7592776368088"
[1] "Skewness in " "word_freq_re" "9.14013068435113"
[1] "Skewness in " "word_freq_edu" "10.1160633745584"
[1] "Skewness in " "word_freq_table" "19.8547388684828"
[1] "Skewness in " "word_freq_conference" "19.707589277451"
[1] "Skewness in " "char_freq_.3B" "13.6996841069832"
[1] "Skewness in " "char_freq_.28" "13.574899154289"
[1] "Skewness in " "char_freq_.5B" "21.0697996565398"
[1] "Skewness in " "char_freq_.21" "18.6458405095097"
[1] "Skewness in " "char_freq_.24" "11.1558633748888"
[1] "Skewness in " "char_freq_.23" "31.0418137486014"
[1] "Skewness in " "capital_run_length_average"
[3] "23.746431702609"
[1] "Skewness in " "capital_run_length_longest"
[3] "30.7449357168226"
[1] "Skewness in " "capital_run_length_total" "8.70417212836709"
[1] "Skewness in " "class" "0.433528573327334"for (i in names(spam)){
hist(spam[,i],main=i,breaks=50)}
A little more EDA
Look at boxplots
for (i in names(spam)){
boxplot(spam[,i],main=i)}
A even a little EDA
Look at correlations
M <- cor(spam)
library(corrplot)
corrplot(M,method="color",order="FPC")
Data Preparation
Convert the class factor to an integer class starting at 0. This is picky, but it’s a requirement for XGBoost
class<-spam$class
label<-as.integer(spam$class)
spam$class<-NULLSplit the data for training and testing (70/20/10 train/test/holdout split)
n<-nrow(spam)
set.seed(123)
train_index<-sample(n,floor(0.70*n))
train_data<-as.matrix(spam[train_index,])
train_label<-label[train_index]
testholdout_data<-spam[-train_index,]
testholdout_label<-label[-train_index]
nth<-nrow(testholdout_data)
th_index<-sample(nth,floor(0.67*nth))
test_data<-as.matrix(testholdout_data[th_index,])
test_label<-testholdout_label[th_index]
holdout_data<-as.matrix(testholdout_data[-th_index,])
holdout_label<-testholdout_label[-th_index]Transform the two data sets into xgb.Matrix
xgb_train<-xgb.DMatrix(data=train_data,label=train_label)
xgb_test<-xgb.DMatrix(data=test_data,label=test_label)Get the number of negative & positive cases in our data
negative_cases <- sum(train_label==0)
postive_cases <- sum(train_label==1)Modeling
XGBoost
Chosen because I am trying to use a different model for every project
First pass
params<-list(booster="gbtree", # default
objective="binary:logistic", # the objective function
eta=0.3,
gamma=0,
max_depth=6, # the maximum depth of each decision tree
min_child_weight=1,
# scale_pos_weight = negative_cases/postive_cases, # NOT DEFAULT control for imbalanced classes
subsample=1,
colsample_bytree=1)
set.seed(123)
xgbcv<-xgb.cv(params=params,
data=xgb_train,
nrounds=100,
nfold=5,
# metrics = list("rmse","auc"),
showsd=T,
stratified=T,
print_every_n=10,
early_stopping_rounds=10,
maximize=F)[18:58:53] WARNING: amalgamation/../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[18:58:53] WARNING: amalgamation/../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[18:58:53] WARNING: amalgamation/../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[18:58:53] WARNING: amalgamation/../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[18:58:53] WARNING: amalgamation/../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[1] train-logloss:0.497973+0.001627 test-logloss:0.506317+0.003253
Multiple eval metrics are present. Will use test_logloss for early stopping.
Will train until test_logloss hasn't improved in 10 rounds.
[11] train-logloss:0.118546+0.004718 test-logloss:0.171150+0.022390
[21] train-logloss:0.075503+0.004618 test-logloss:0.146909+0.020544
[31] train-logloss:0.058049+0.004332 test-logloss:0.140582+0.021747
[41] train-logloss:0.045287+0.003136 test-logloss:0.139221+0.023500
[51] train-logloss:0.036392+0.002715 test-logloss:0.139679+0.026465
Stopping. Best iteration:
[42] train-logloss:0.044432+0.002995 test-logloss:0.139057+0.023487Review model
xgbcv##### xgb.cv 5-foldsBest iteration:min(xgbcv$evaluation_log$test_logloss_mean)[1] 0.1390574Train the XGBoost classifer
set.seed(123)
xgb1<-xgb.train(params=params,
data=xgb_train,
nrounds=42,
watchlist=list(val=xgb_test,train=xgb_train),
print_every_n=10,
early_stopping_rounds=10,
maximize=F,
eval_metric="error") # 'logloss'[1] val-error:0.084324 train-error:0.071429
Multiple eval metrics are present. Will use train_error for early stopping.
Will train until train_error hasn't improved in 10 rounds.
[11] val-error:0.058378 train-error:0.031056
[21] val-error:0.052973 train-error:0.024845
[31] val-error:0.050811 train-error:0.017702
[41] val-error:0.047568 train-error:0.013975
[42] val-error:0.047568 train-error:0.012422 Predict outcomes with the test data
xgb1_pred<-predict(xgb1,test_data)
my_roc1<-roc(test_label,xgb1_pred)Setting levels: control = 0, case = 1
Setting direction: controls < casesplot(my_roc1,print.thres="best",print.thres.best.method="youden",
print.thres.best.weights=c(50,0.2))
coords(my_roc1,"best",ret=c("threshold","specificity","sensitivity","accuracy",
"precision","recall"),transpose=FALSE)importance_matrix1<-xgb.importance(model=xgb1)
print(importance_matrix1)xgb.plot.importance(importance_matrix=importance_matrix1)
xgb.dump(xgb1,with_stats=TRUE) [1] "booster[0]"
[2] "0:[f52<0.0555000007] yes=1,no=2,missing=1,gain=1032.54187,cover=805"
[3] "1:[f6<0.0549999997] yes=3,no=4,missing=3,gain=452.154358,cover=607.75"
[4] "3:[f51<0.459500015] yes=7,no=8,missing=7,gain=236.422852,cover=549.75"
[5] "7:[f15<0.199999988] yes=15,no=16,missing=15,gain=65.284668,cover=492.5"
[6] "15:[f23<0.00999999978] yes=23,no=24,missing=23,gain=60.5469971,cover=450.25"
[7] "23:[f21<0.135000005] yes=35,no=36,missing=35,gain=19.4257812,cover=436.25"
[8] "35:leaf=-0.535191655,cover=429.5"
[9] "36:leaf=-0.0193548389,cover=6.75"
[10] "24:[f45<0.0799999982] yes=37,no=38,missing=37,gain=13.4761896,cover=14"
[11] "37:leaf=0.24000001,cover=11.5"
[12] "38:leaf=-0.428571463,cover=2.5"
[13] "16:[f4<1.12] yes=25,no=26,missing=25,gain=21.15201,cover=42.25"
[14] "25:[f51<0.105999999] yes=39,no=40,missing=39,gain=12.4953976,cover=34.25"
[15] "39:leaf=-0.349450588,cover=21.75"
[16] "40:leaf=0.0222222228,cover=12.5"
[17] "26:[f20<0.449999988] yes=41,no=42,missing=41,gain=8.73333359,cover=8"
[18] "41:leaf=-0.24000001,cover=1.5"
[19] "42:leaf=0.440000027,cover=6.5"
[20] "8:[f56<58.5] yes=17,no=18,missing=17,gain=68.9557343,cover=57.25"
[21] "17:[f54<2.65399981] yes=27,no=28,missing=27,gain=20.1228333,cover=27.25"
[22] "27:[f51<1.28299999] yes=43,no=44,missing=43,gain=10.004631,cover=22"
[23] "43:leaf=-0.420895547,cover=15.75"
[24] "44:leaf=0.0206896551,cover=6.25"
[25] "28:[f55<13] yes=45,no=46,missing=45,gain=1.28761864,cover=5.25"
[26] "45:leaf=0.371428609,cover=4.25"
[27] "46:leaf=-0,cover=1"
[28] "18:[f24<0.224999994] yes=29,no=30,missing=29,gain=9.27107239,cover=30"
[29] "29:[f51<3.80949998] yes=47,no=48,missing=47,gain=2.98165894,cover=28.75"
[30] "47:leaf=0.526956558,cover=27.75"
[31] "48:leaf=-0,cover=1"
[32] "30:leaf=-0.200000018,cover=1.25"
[33] "4:[f26<0.140000001] yes=9,no=10,missing=9,gain=33.6090393,cover=58"
[34] "9:[f24<0.300000012] yes=19,no=20,missing=19,gain=5.72929382,cover=55.25"
[35] "19:[f45<0.115000002] yes=31,no=32,missing=31,gain=5.99990845,cover=54"
[36] "31:[f9<1.75] yes=49,no=50,missing=49,gain=1.83082581,cover=52.75"
[37] "49:leaf=0.559808612,cover=51.25"
[38] "50:leaf=0.120000005,cover=1.5"
[39] "32:leaf=-0.0666666701,cover=1.25"
[40] "20:leaf=-0.0666666701,cover=1.25"
[41] "10:leaf=-0.440000027,cover=2.75"
[42] "2:[f24<0.399999976] yes=5,no=6,missing=5,gain=123.17572,cover=197.25"
[43] "5:[f45<0.49000001] yes=11,no=12,missing=11,gain=25.8093872,cover=185.25"
[44] "11:[f41<0.189999998] yes=21,no=22,missing=21,gain=20.435791,cover=183.25"
[45] "21:[f26<0.210000008] yes=33,no=34,missing=33,gain=14.0670166,cover=181.25"
[46] "33:[f55<5.5] yes=51,no=52,missing=51,gain=6.9677124,cover=179.75"
[47] "51:leaf=-0,cover=2"
[48] "52:leaf=0.563076973,cover=177.75"
[49] "34:leaf=-0.24000001,cover=1.5"
[50] "22:leaf=-0.300000012,cover=2"
[51] "12:leaf=-0.400000036,cover=2"
[52] "6:[f17<0.275000006] yes=13,no=14,missing=13,gain=14.3489761,cover=12"
[53] "13:leaf=-0.548936188,cover=10.75"
[54] "14:leaf=0.333333373,cover=1.25"
[55] "booster[1]"
[56] "0:[f51<0.0785000026] yes=1,no=2,missing=1,gain=586.995667,cover=753.449585"
[57] "1:[f6<0.0199999996] yes=3,no=4,missing=3,gain=117.281952,cover=439.121918"
[58] "3:[f52<0.164000005] yes=7,no=8,missing=7,gain=58.6425781,cover=409.310303"
[59] "7:[f15<0.105000004] yes=15,no=16,missing=15,gain=24.293396,cover=394.83606"
[60] "15:[f23<0.00999999978] yes=29,no=30,missing=29,gain=11.4551392,cover=361.05835"
[61] "29:[f54<9.37700081] yes=49,no=50,missing=49,gain=5.21472168,cover=351.143005"
[62] "49:leaf=-0.421427369,cover=346.66806"
[63] "50:leaf=-0.0760556608,cover=4.47494745"
[64] "30:[f49<0.127999991] yes=51,no=52,missing=51,gain=9.65857983,cover=9.9153471"
[65] "51:leaf=0.115596868,cover=6.99521017"
[66] "52:leaf=-0.461250991,cover=2.92013669"
[67] "16:[f4<1.08999991] yes=31,no=32,missing=31,gain=15.7878151,cover=33.7776871"
[68] "31:[f24<0.0649999976] yes=53,no=54,missing=53,gain=6.51610565,cover=27.809351"
[69] "53:leaf=-0.127097696,cover=19.7167416"
[70] "54:leaf=-0.444941849,cover=8.09260845"
[71] "32:[f20<0.675000012] yes=55,no=56,missing=55,gain=5.45510101,cover=5.96833467"
[72] "55:leaf=-0.184720382,cover=1.22401714"
[73] "56:leaf=0.404648811,cover=4.74431753"
[74] "8:[f49<0.291000009] yes=17,no=18,missing=17,gain=5.74369907,cover=14.4742575"
[75] "17:[f20<0.344999999] yes=33,no=34,missing=33,gain=3.37833309,cover=11.9062004"
[76] "33:[f54<3.17449999] yes=57,no=58,missing=57,gain=1.99806023,cover=4.50824881"
[77] "57:leaf=-0.0983964205,cover=2.41232681"
[78] "58:leaf=0.238610685,cover=2.09592223"
[79] "34:leaf=0.414770663,cover=7.3979516"
[80] "18:[f52<0.418500006] yes=35,no=36,missing=35,gain=0.973577857,cover=2.56805634"
[81] "35:leaf=-0.25842607,cover=1.40216386"
[82] "36:leaf=0.0444736555,cover=1.16589248"
[83] "4:[f26<0.0799999982] yes=9,no=10,missing=9,gain=18.5142155,cover=29.8116283"
[84] "9:[f24<0.150000006] yes=19,no=20,missing=19,gain=4.85281754,cover=26.9512081"
[85] "19:[f6<1.70499992] yes=37,no=38,missing=37,gain=2.89380264,cover=24.7824516"
[86] "37:[f55<9.5] yes=59,no=60,missing=59,gain=3.37735367,cover=22.9144287"
[87] "59:leaf=0.00158719451,cover=1.86885166"
[88] "60:leaf=0.422479719,cover=21.0455761"
[89] "38:leaf=0.00127137615,cover=1.86802447"
[90] "20:leaf=-0.0753581226,cover=2.16875601"
[91] "10:leaf=-0.388159364,cover=2.86042023"
[92] "2:[f55<18.5] yes=5,no=6,missing=5,gain=167.783585,cover=314.327667"
[93] "5:[f15<0.295000017] yes=11,no=12,missing=11,gain=61.4741745,cover=120.981651"
[94] "11:[f52<0.0964999944] yes=21,no=22,missing=21,gain=32.2640343,cover=89.6300507"
[95] "21:[f6<0.0700000003] yes=39,no=40,missing=39,gain=23.6711578,cover=78.6855087"
[96] "39:[f18<3.09499979] yes=61,no=62,missing=61,gain=17.210083,cover=72.5365829"
[97] "61:leaf=-0.395093113,cover=51.8597794"
[98] "62:leaf=-0.0684513748,cover=20.6768036"
[99] "40:[f56<140.5] yes=63,no=64,missing=63,gain=2.86864614,cover=6.14892626"
[100] "63:leaf=0.363841712,cover=4.94841576"
[101] "64:leaf=-0.0860577375,cover=1.20051038"
[102] "22:[f54<1.7385] yes=41,no=42,missing=41,gain=4.40785313,cover=10.944541"
[103] "41:leaf=-0.0988789797,cover=1.89926744"
[104] "42:[f11<0.840000033] yes=65,no=66,missing=65,gain=0.121609688,cover=9.04527378"
[105] "65:leaf=0.387799919,cover=7.17964983"
[106] "66:leaf=0.143488899,cover=1.86562407"
[107] "12:[f26<0.425000012] yes=23,no=24,missing=23,gain=12.188467,cover=31.3516026"
[108] "23:[f55<6.5] yes=43,no=44,missing=43,gain=4.41737366,cover=29.8634777"
[109] "43:[f56<54.5] yes=67,no=68,missing=67,gain=3.36038685,cover=5.81191778"
[110] "67:leaf=-0.0785029978,cover=3.91306901"
[111] "68:leaf=0.33301872,cover=1.89884889"
[112] "44:[f51<0.119000003] yes=69,no=70,missing=69,gain=0.853233337,cover=24.0515594"
[113] "69:leaf=0.101548865,cover=1.64883316"
[114] "70:leaf=0.40932712,cover=22.4027271"
[115] "24:leaf=-0.378120273,cover=1.48812568"
[116] "6:[f24<0.389999986] yes=13,no=14,missing=13,gain=40.0128174,cover=193.346024"
[117] "13:[f45<0.140000001] yes=25,no=26,missing=25,gain=15.8141479,cover=186.028503"
[118] "25:[f26<0.569999993] yes=45,no=46,missing=45,gain=9.95718384,cover=182.003387"
[119] "45:[f36<0.199999988] yes=71,no=72,missing=71,gain=7.45324707,cover=180.372452"
[120] "71:leaf=0.449986309,cover=169.621643"
[121] "72:leaf=0.170551509,cover=10.750823"
[122] "46:leaf=-0.210337609,cover=1.63093328"
[123] "26:[f52<0.0199999996] yes=47,no=48,missing=47,gain=7.30005693,cover=4.02510929"
[124] "47:leaf=-0.392340511,cover=2.61945701"
[125] "48:leaf=0.290670395,cover=1.4056524"
[126] "14:[f9<0.120000005] yes=27,no=28,missing=27,gain=6.58013964,cover=7.31752682"
[127] "27:leaf=-0.412040442,cover=5.62634706"
[128] "28:leaf=0.174385846,cover=1.69117987"
[129] "booster[2]"
[130] "0:[f51<0.0785000026] yes=1,no=2,missing=1,gain=347.717041,cover=663.767151"
[131] "1:[f6<0.0199999996] yes=3,no=4,missing=3,gain=69.5987549,cover=384.809631"
[132] "3:[f52<0.164000005] yes=7,no=8,missing=7,gain=36.2735291,cover=358.285217"
[133] "7:[f15<0.105000004] yes=15,no=16,missing=15,gain=16.6748657,cover=345.004395"
[134] "15:[f22<0.114999995] yes=27,no=28,missing=27,gain=9.59564209,cover=313.30304"
[135] "27:[f54<8.37699986] yes=45,no=46,missing=45,gain=7.76104736,cover=306.09137"
[136] "45:leaf=-0.353589088,cover=300.346588"
[137] "46:leaf=-0.0013506012,cover=5.74476528"
[138] "28:[f5<0.0250000004] yes=47,no=48,missing=47,gain=9.7678566,cover=7.21169186"
[139] "47:leaf=-0.297207415,cover=3.74658585"
[140] "48:leaf=0.320930898,cover=3.46510625"
[141] "16:[f4<1.08999991] yes=29,no=30,missing=29,gain=9.3172493,cover=31.7013474"
[142] "29:[f44<0.00499999989] yes=49,no=50,missing=49,gain=6.16841507,cover=26.3267956"
[143] "49:leaf=-0.0573044382,cover=16.1318665"
[144] "50:leaf=-0.351599663,cover=10.1949291"
[145] "30:[f20<0.675000012] yes=51,no=52,missing=51,gain=3.8065269,cover=5.3745513"
[146] "51:leaf=-0.155984282,cover=1.20219588"
[147] "52:leaf=0.344260812,cover=4.17235518"
[148] "8:[f24<0.239999995] yes=17,no=18,missing=17,gain=4.61157894,cover=13.2808123"
[149] "17:[f54<1.68300009] yes=31,no=32,missing=31,gain=3.66770601,cover=12.1439104"
[150] "31:leaf=-0.162829369,cover=1.22264493"
[151] "32:[f49<0.291999996] yes=53,no=54,missing=53,gain=1.96172047,cover=10.9212656"
[152] "53:leaf=0.337027907,cover=9.26130295"
[153] "54:leaf=-0.00912707392,cover=1.65996277"
[154] "18:leaf=-0.257846504,cover=1.13690138"
[155] "4:[f26<0.0799999982] yes=9,no=10,missing=9,gain=11.9225912,cover=26.5244255"
[156] "9:[f48<0.0724999979] yes=19,no=20,missing=19,gain=3.40479088,cover=23.9334793"
[157] "19:[f24<0.150000006] yes=33,no=34,missing=33,gain=3.02401352,cover=22.6570549"
[158] "33:[f52<0.526000023] yes=55,no=56,missing=55,gain=2.44464493,cover=20.965271"
[159] "55:leaf=0.346505553,cover=19.9092999"
[160] "56:leaf=-0.0670550466,cover=1.05597198"
[161] "34:leaf=-0.06108585,cover=1.69178355"
[162] "20:leaf=-0.131299689,cover=1.27642381"
[163] "10:leaf=-0.328483611,cover=2.59094644"
[164] "2:[f52<0.00650000013] yes=5,no=6,missing=5,gain=113.063835,cover=278.95752"
[165] "5:[f54<2.75449991] yes=11,no=12,missing=11,gain=59.1300468,cover=146.453339"
[166] "11:[f6<0.0900000036] yes=21,no=22,missing=21,gain=29.6809502,cover=91.0014877"
[167] "21:[f51<0.807000041] yes=35,no=36,missing=35,gain=19.9298439,cover=80.2316055"
[168] "35:[f15<1.31500006] yes=57,no=58,missing=57,gain=14.5440254,cover=65.0512238"
[169] "57:leaf=-0.325599611,cover=61.2669868"
[170] "58:leaf=0.234102592,cover=3.78423262"
[171] "36:[f56<14.5] yes=59,no=60,missing=59,gain=7.6202445,cover=15.1803818"
[172] "59:leaf=-0.181108236,cover=5.34436321"
[173] "60:leaf=0.234999925,cover=9.83601952"
[174] "22:[f18<0.735000014] yes=37,no=38,missing=37,gain=1.78099918,cover=10.7698841"
[175] "37:leaf=0.0100570321,cover=1.76817203"
[176] "38:[f0<0.0500000007] yes=61,no=62,missing=61,gain=0.573526382,cover=9.0017128"
[177] "61:leaf=0.382601231,cover=7.13444662"
[178] "62:leaf=0.109364092,cover=1.86726582"
[179] "12:[f24<0.104999997] yes=23,no=24,missing=23,gain=11.8041763,cover=55.4518433"
[180] "23:[f45<0.349999994] yes=39,no=40,missing=39,gain=10.0466461,cover=51.3340149"
[181] "39:[f41<0.669999957] yes=63,no=64,missing=63,gain=6.58639145,cover=49.55233"
[182] "63:leaf=0.319452792,cover=48.2961922"
[183] "64:leaf=-0.242561311,cover=1.25613689"
[184] "40:leaf=-0.308321327,cover=1.78168571"
[185] "24:[f9<0.0399999991] yes=41,no=42,missing=41,gain=2.66851139,cover=4.11782885"
[186] "41:leaf=-0.336669952,cover=2.97884989"
[187] "42:leaf=0.111120552,cover=1.13897896"
[188] "6:[f24<1.47000003] yes=13,no=14,missing=13,gain=11.6284637,cover=132.504196"
[189] "13:[f41<0.189999998] yes=25,no=26,missing=25,gain=5.20454407,cover=130.90567"
[190] "25:[f45<0.400000006] yes=43,no=44,missing=43,gain=1.86880493,cover=129.462997"
[191] "43:[f25<0.25] yes=65,no=66,missing=65,gain=1.36662292,cover=128.349564"
[192] "65:leaf=0.397476047,cover=127.249908"
[193] "66:leaf=0.0309593324,cover=1.09964728"
[194] "44:leaf=0.00224368367,cover=1.11343408"
[195] "26:leaf=-0.115344249,cover=1.44267893"
[196] "14:leaf=-0.297277927,cover=1.59852087"
[197] "booster[3]"
[198] "0:[f51<0.0785000026] yes=1,no=2,missing=1,gain=214.292755,cover=569.169922"
[199] "1:[f6<0.0199999996] yes=3,no=4,missing=3,gain=42.3616486,cover=329.164276"
[200] "3:[f52<0.0874999985] yes=7,no=8,missing=7,gain=22.6076813,cover=305.975494"
[201] "7:[f26<0.00499999989] yes=15,no=16,missing=15,gain=13.1420593,cover=287.281128"
[202] "15:[f24<0.0199999996] yes=27,no=28,missing=27,gain=15.8955383,cover=210.440582"
[203] "27:[f55<9.5] yes=41,no=42,missing=41,gain=33.7884712,cover=143.350784"
[204] "41:leaf=-0.296826243,cover=83.8248901"
[205] "42:leaf=-0.00194381899,cover=59.5259018"
[206] "28:[f10<0.370000005] yes=43,no=44,missing=43,gain=3.10641479,cover=67.0897903"
[207] "43:leaf=-0.362664968,cover=66.0281754"
[208] "44:leaf=0.0864094198,cover=1.06161046"
[209] "16:leaf=-0.382458061,cover=76.8405457"
[210] "8:[f24<0.239999995] yes=17,no=18,missing=17,gain=7.51485777,cover=18.6943607"
[211] "17:[f54<3.32500005] yes=29,no=30,missing=29,gain=5.37170696,cover=15.771348"
[212] "29:[f4<0.075000003] yes=45,no=46,missing=45,gain=6.66941309,cover=9.07005787"
[213] "45:leaf=-0.210498855,cover=5.28910208"
[214] "46:leaf=0.259623021,cover=3.78095603"
[215] "30:[f55<102.5] yes=47,no=48,missing=47,gain=0.260046005,cover=6.70129013"
[216] "47:leaf=0.358501554,cover=5.67066145"
[217] "48:leaf=0.0800087005,cover=1.03062832"
[218] "18:leaf=-0.315147281,cover=2.92301345"
[219] "4:[f26<0.0799999982] yes=9,no=10,missing=9,gain=7.88185978,cover=23.1887817"
[220] "9:[f32<0.199999988] yes=19,no=20,missing=19,gain=3.23519993,cover=20.9194946"
[221] "19:[f6<1.70499992] yes=31,no=32,missing=31,gain=2.26970387,cover=18.9524574"
[222] "31:[f0<0.514999986] yes=49,no=50,missing=49,gain=1.63389397,cover=17.2216949"
[223] "49:leaf=0.312675804,cover=16.0762959"
[224] "50:leaf=-0.0319540724,cover=1.14539766"
[225] "32:leaf=-0.0464014299,cover=1.73076272"
[226] "20:leaf=-0.103778757,cover=1.96703708"
[227] "10:leaf=-0.287403256,cover=2.26928759"
[228] "2:[f52<0.00650000013] yes=5,no=6,missing=5,gain=75.6320419,cover=240.005661"
[229] "5:[f4<0.254999995] yes=11,no=12,missing=11,gain=39.2307587,cover=131.286469"
[230] "11:[f15<0.784999967] yes=21,no=22,missing=21,gain=28.623539,cover=85.6243515"
[231] "21:[f51<0.49849999] yes=33,no=34,missing=33,gain=16.8417301,cover=73.1617889"
[232] "33:[f6<0.0949999988] yes=51,no=52,missing=51,gain=8.36207962,cover=46.4586792"
[233] "51:leaf=-0.338726342,cover=43.2456512"
[234] "52:leaf=0.129480585,cover=3.21302629"
[235] "34:[f56<22.5] yes=53,no=54,missing=53,gain=10.1339865,cover=26.7031136"
[236] "53:leaf=-0.198360413,cover=12.2624254"
[237] "54:leaf=0.159192279,cover=14.4406891"
[238] "22:[f54<1.61249995] yes=35,no=36,missing=35,gain=4.90404797,cover=12.4625578"
[239] "35:[f56<50] yes=55,no=56,missing=55,gain=2.21353579,cover=3.17560339"
[240] "55:leaf=-0.186344817,cover=2.14998603"
[241] "56:leaf=0.216664895,cover=1.02561712"
[242] "36:leaf=0.380647719,cover=9.28695488"
[243] "12:[f26<0.254999995] yes=23,no=24,missing=23,gain=17.5660076,cover=45.6621246"
[244] "23:[f41<0.479999989] yes=37,no=38,missing=37,gain=11.5462456,cover=41.5966797"
[245] "37:[f44<0.644999981] yes=57,no=58,missing=57,gain=6.34112549,cover=39.3078995"
[246] "57:leaf=0.348971784,cover=36.2264214"
[247] "58:leaf=-0.0769368634,cover=3.08147764"
[248] "38:leaf=-0.290545374,cover=2.28877926"
[249] "24:leaf=-0.324250013,cover=4.06544495"
[250] "6:[f24<1.47000003] yes=13,no=14,missing=13,gain=8.42391968,cover=108.719193"
[251] "13:[f41<0.189999998] yes=25,no=26,missing=25,gain=3.92625427,cover=107.265007"
[252] "25:[f45<0.400000006] yes=39,no=40,missing=39,gain=1.48846436,cover=105.830376"
[253] "39:[f25<0.25] yes=59,no=60,missing=59,gain=1.10977173,cover=104.717384"
[254] "59:leaf=0.355515271,cover=103.621758"
[255] "60:leaf=0.0261539109,cover=1.095626"
[256] "40:leaf=0.00188951578,cover=1.11299157"
[257] "26:leaf=-0.0952680036,cover=1.43463123"
[258] "14:leaf=-0.259161472,cover=1.45418596"
[259] "booster[4]"
[260] "0:[f52<0.0185000002] yes=1,no=2,missing=1,gain=136.764374,cover=485.534088"
[261] "1:[f6<0.0399999991] yes=3,no=4,missing=3,gain=54.8624268,cover=354.654663"
[262] "3:[f51<0.534500003] yes=7,no=8,missing=7,gain=38.0993042,cover=324.756531"
[263] "7:[f26<0.00499999989] yes=15,no=16,missing=15,gain=15.9963837,cover=287.971008"
[264] "15:[f4<0.86500001] yes=27,no=28,missing=27,gain=20.3069,cover=222.004578"
[265] "27:[f54<2.30349994] yes=43,no=44,missing=43,gain=11.4795837,cover=202.25441"
[266] "43:leaf=-0.269867003,cover=127.980591"
[267] "44:leaf=-0.119179159,cover=74.2738113"
[268] "28:[f20<0.805000007] yes=45,no=46,missing=45,gain=13.3923988,cover=19.7501793"
[269] "45:leaf=-0.146717593,cover=9.6489439"
[270] "46:leaf=0.326025605,cover=10.1012344"
[271] "16:leaf=-0.35724467,cover=65.966423"
[272] "8:[f56<22.5] yes=17,no=18,missing=17,gain=14.4809036,cover=36.7855301"
[273] "17:[f51<1.74650002] yes=29,no=30,missing=29,gain=7.91161013,cover=12.0143747"
[274] "29:[f56<18.5] yes=47,no=48,missing=47,gain=0.911518097,cover=8.9987936"
[275] "47:leaf=-0.340687633,cover=7.39217901"
[276] "48:leaf=-0.0536869802,cover=1.60661471"
[277] "30:[f51<2.95949984] yes=49,no=50,missing=49,gain=0.535254717,cover=3.01558113"
[278] "49:leaf=0.0176849999,cover=1.14577186"
[279] "50:leaf=0.277139485,cover=1.86980915"
[280] "18:[f45<0.140000001] yes=31,no=32,missing=31,gain=6.39363098,cover=24.7711544"
[281] "31:[f44<1.245] yes=51,no=52,missing=51,gain=3.86320877,cover=23.5850143"
[282] "51:leaf=0.287455231,cover=22.548624"
[283] "52:leaf=-0.181834936,cover=1.03638887"
[284] "32:leaf=-0.296939671,cover=1.18614042"
[285] "4:[f24<0.230000004] yes=9,no=10,missing=9,gain=7.0106945,cover=29.898138"
[286] "9:[f51<0.0455000028] yes=19,no=20,missing=19,gain=2.31308556,cover=27.8416843"
[287] "19:[f6<0.894999981] yes=33,no=34,missing=33,gain=2.38335419,cover=6.66828442"
[288] "33:[f18<1.43000007] yes=53,no=54,missing=53,gain=2.69864964,cover=3.11622238"
[289] "53:leaf=-0.275870979,cover=1.69877052"
[290] "54:leaf=0.165578112,cover=1.41745186"
[291] "34:[f17<0.63499999] yes=55,no=56,missing=55,gain=0.129532814,cover=3.55206203"
[292] "55:leaf=0.272106171,cover=2.52027893"
[293] "56:leaf=0.0602293797,cover=1.0317831"
[294] "20:[f6<0.254999995] yes=35,no=36,missing=35,gain=0.87909317,cover=21.173399"
[295] "35:[f1<0.210000008] yes=57,no=58,missing=57,gain=1.91012931,cover=3.74068332"
[296] "57:leaf=0.257958919,cover=2.4062748"
[297] "58:leaf=-0.108740628,cover=1.33440852"
[298] "36:leaf=0.347378701,cover=17.4327164"
[299] "10:[f2<0.0599999987] yes=21,no=22,missing=21,gain=0.0133974552,cover=2.0564537"
[300] "21:leaf=-0.270590097,cover=1.03485596"
[301] "22:leaf=-0.0715219006,cover=1.02159762"
[302] "2:[f24<0.199999988] yes=5,no=6,missing=5,gain=51.0172348,cover=130.879425"
[303] "5:[f45<0.49000001] yes=11,no=12,missing=11,gain=10.9381332,cover=111.292732"
[304] "11:[f55<9.5] yes=23,no=24,missing=23,gain=8.23155212,cover=109.251076"
[305] "23:[f17<0.254999995] yes=37,no=38,missing=37,gain=5.48771715,cover=6.43299007"
[306] "37:[f44<0.159999996] yes=59,no=60,missing=59,gain=1.71530628,cover=4.12969112"
[307] "59:leaf=-0.00227741268,cover=1.99818659"
[308] "60:leaf=-0.359174699,cover=2.13150454"
[309] "38:leaf=0.275575966,cover=2.30329895"
[310] "24:[f49<0.652500033] yes=39,no=40,missing=39,gain=3.20432281,cover=102.818085"
[311] "39:[f45<0.185000002] yes=61,no=62,missing=61,gain=1.87688446,cover=101.210045"
[312] "61:leaf=0.327173501,cover=99.7350769"
[313] "62:leaf=-0.0082389079,cover=1.47496486"
[314] "40:leaf=-0.0678494573,cover=1.60803759"
[315] "12:leaf=-0.304128796,cover=2.04165697"
[316] "6:[f51<0.833500028] yes=13,no=14,missing=13,gain=5.71845245,cover=19.5866947"
[317] "13:[f13<0.340000004] yes=25,no=26,missing=25,gain=3.71430397,cover=18.0547237"
[318] "25:[f9<0.5] yes=41,no=42,missing=41,gain=1.38800812,cover=16.9576187"
[319] "41:[f16<0.694999993] yes=63,no=64,missing=63,gain=0.568202972,cover=14.9345369"
[320] "63:leaf=-0.364171684,cover=13.8471231"
[321] "64:leaf=-0.0640014261,cover=1.08741403"
[322] "42:[f36<0.245000005] yes=65,no=66,missing=65,gain=0.370436162,cover=2.02308106"
[323] "65:leaf=0.053496331,cover=1.02066422"
[324] "66:leaf=-0.134286836,cover=1.00241685"
[325] "26:leaf=0.149910137,cover=1.09710515"
[326] "14:leaf=0.221014619,cover=1.53197074"
[327] "booster[5]"
[328] "0:[f51<0.0795000046] yes=1,no=2,missing=1,gain=88.2819061,cover=414.7677"
[329] "1:[f24<0.0949999988] yes=3,no=4,missing=3,gain=23.4115601,cover=241.42569"
[330] "3:[f55<9.5] yes=7,no=8,missing=7,gain=41.4260559,cover=169.595337"
[331] "7:[f7<0.295000017] yes=15,no=16,missing=15,gain=11.1531143,cover=85.5722046"
[332] "15:[f17<1.875] yes=29,no=30,missing=29,gain=5.11936951,cover=82.6856155"
[333] "29:[f18<9.30500031] yes=51,no=52,missing=51,gain=2.2902298,cover=81.0636368"
[334] "51:leaf=-0.288352519,cover=79.9115601"
[335] "52:leaf=0.0802352652,cover=1.15207505"
[336] "30:leaf=0.175999224,cover=1.62198055"
[337] "16:[f18<0.720000029] yes=31,no=32,missing=31,gain=5.76569271,cover=2.88658977"
[338] "31:leaf=-0.128173783,cover=1.47473574"
[339] "32:leaf=0.5576635,cover=1.41185391"
[340] "8:[f45<0.0450000018] yes=17,no=18,missing=17,gain=25.997633,cover=84.0231323"
[341] "17:[f26<0.0350000001] yes=33,no=34,missing=33,gain=20.3240623,cover=61.4334526"
[342] "33:[f4<0.0549999997] yes=53,no=54,missing=53,gain=11.7715206,cover=54.9570312"
[343] "53:leaf=0.0892438591,cover=33.2750435"
[344] "54:leaf=0.373271376,cover=21.6819859"
[345] "34:leaf=-0.323674023,cover=6.47641993"
[346] "18:[f27<0.229999989] yes=35,no=36,missing=35,gain=28.1851006,cover=22.5896816"
[347] "35:[f13<0.0399999991] yes=55,no=56,missing=55,gain=0.742815018,cover=20.2170143"
[348] "55:leaf=-0.357319355,cover=19.1716919"
[349] "56:leaf=-0.0460176058,cover=1.04532218"
[350] "36:leaf=0.598303497,cover=2.37266779"
[351] "4:[f10<0.419999987] yes=9,no=10,missing=9,gain=4.82080078,cover=71.8303452"
[352] "9:[f56<2034.5] yes=19,no=20,missing=19,gain=3.10401917,cover=70.7224731"
[353] "19:[f1<0.495000005] yes=37,no=38,missing=37,gain=0.597427368,cover=69.6088715"
[354] "37:leaf=-0.332520425,cover=67.7974701"
[355] "38:leaf=-0.0894687772,cover=1.81140471"
[356] "20:leaf=0.102761403,cover=1.11359572"
[357] "10:leaf=0.188417524,cover=1.10787809"
[358] "2:[f20<0.414999992] yes=5,no=6,missing=5,gain=42.0314102,cover=173.341995"
[359] "5:[f54<3.74100018] yes=11,no=12,missing=11,gain=18.1069565,cover=62.799675"
[360] "11:[f7<0.50999999] yes=21,no=22,missing=21,gain=10.5610094,cover=48.0153732"
[361] "21:[f51<0.511000037] yes=39,no=40,missing=39,gain=6.62225533,cover=45.2226334"
[362] "39:[f15<1.33500004] yes=57,no=58,missing=57,gain=2.8802681,cover=27.6359577"
[363] "57:leaf=-0.318110406,cover=26.2960243"
[364] "58:leaf=0.0815281942,cover=1.33993328"
[365] "40:[f54<2.64050007] yes=59,no=60,missing=59,gain=3.17068124,cover=17.5866737"
[366] "59:leaf=-0.122323729,cover=14.0460901"
[367] "60:leaf=0.16533564,cover=3.54058361"
[368] "22:leaf=0.321195781,cover=2.79274035"
[369] "12:[f11<0.725000024] yes=23,no=24,missing=23,gain=3.51822948,cover=14.7843018"
[370] "23:[f53<0.0434999987] yes=41,no=42,missing=41,gain=2.21885777,cover=12.6113634"
[371] "41:[f9<1.125] yes=61,no=62,missing=61,gain=0.505699158,cover=10.3970757"
[372] "61:leaf=0.334518552,cover=9.19517517"
[373] "62:leaf=0.061931368,cover=1.20190048"
[374] "42:[f54<6.75500011] yes=63,no=64,missing=63,gain=2.35436606,cover=2.21428728"
[375] "63:leaf=-0.237252429,cover=1.07031786"
[376] "64:leaf=0.211391285,cover=1.14396942"
[377] "24:[f55<53.5] yes=43,no=44,missing=43,gain=1.08101165,cover=2.17293835"
[378] "43:leaf=-0.246404767,cover=1.08381534"
[379] "44:leaf=0.0738979727,cover=1.08912301"
[380] "6:[f56<69.5] yes=13,no=14,missing=13,gain=14.9265594,cover=110.542328"
[381] "13:[f16<0.185000002] yes=25,no=26,missing=25,gain=5.54903316,cover=19.794796"
[382] "25:[f15<0.774999976] yes=45,no=46,missing=45,gain=4.72299147,cover=17.1958199"
[383] "45:[f17<0.420000017] yes=65,no=66,missing=65,gain=3.08096218,cover=13.6061831"
[384] "65:leaf=-0.22662957,cover=10.8272886"
[385] "66:leaf=0.0974264741,cover=2.7788949"
[386] "46:[f9<0.234999999] yes=67,no=68,missing=67,gain=1.10140681,cover=3.58963704"
[387] "67:leaf=0.275191247,cover=2.52105021"
[388] "68:leaf=-0.0402985923,cover=1.06858671"
[389] "26:leaf=0.329338163,cover=2.59897518"
[390] "14:[f24<0.155000001] yes=27,no=28,missing=27,gain=10.0504227,cover=90.7475281"
[391] "27:[f45<0.204999998] yes=47,no=48,missing=47,gain=4.69523621,cover=86.2331848"
[392] "47:[f44<0.375] yes=69,no=70,missing=69,gain=2.14694977,cover=83.5677032"
[393] "69:leaf=0.331939965,cover=71.0247269"
[394] "70:leaf=0.172870651,cover=12.5429745"
[395] "48:[f23<0.135000005] yes=71,no=72,missing=71,gain=1.37565649,cover=2.66548252"
[396] "71:leaf=-0.20519188,cover=1.56690204"
[397] "72:leaf=0.12742576,cover=1.09858048"
[398] "28:[f49<0.0995000005] yes=49,no=50,missing=49,gain=2.55624533,cover=4.51434278"
[399] "49:leaf=0.110978596,cover=1.75940573"
[400] "50:leaf=-0.282182783,cover=2.7549367"
[401] "booster[6]"
[402] "0:[f52<0.0175000001] yes=1,no=2,missing=1,gain=57.2542191,cover=353.87323"
[403] "1:[f24<0.0299999993] yes=3,no=4,missing=3,gain=29.7881317,cover=264.597656"
[404] "3:[f55<10.5] yes=7,no=8,missing=7,gain=30.3302212,cover=205.52829"
[405] "7:[f16<0.350000024] yes=15,no=16,missing=15,gain=16.3392372,cover=100.908546"
[406] "15:[f10<0.355000019] yes=25,no=26,missing=25,gain=10.9281273,cover=96.862999"
[407] "25:[f6<0.894999981] yes=41,no=42,missing=41,gain=6.3153801,cover=94.8340454"
[408] "41:leaf=-0.227005929,cover=93.0843811"
[409] "42:leaf=0.252550274,cover=1.74965739"
[410] "26:leaf=0.37463659,cover=2.02895737"
[411] "16:[f18<1.39999998] yes=27,no=28,missing=27,gain=0.877241135,cover=4.04554701"
[412] "27:leaf=0.0573700927,cover=1.07984042"
[413] "28:leaf=0.427503139,cover=2.96570659"
[414] "8:[f36<0.194999993] yes=17,no=18,missing=17,gain=24.5057831,cover=104.619736"
[415] "17:[f45<0.0450000018] yes=29,no=30,missing=29,gain=14.3152723,cover=88.1739655"
[416] "29:[f11<1.92499995] yes=43,no=44,missing=43,gain=9.68733025,cover=74.4675064"
[417] "43:leaf=0.202593744,cover=68.5503922"
[418] "44:leaf=-0.175751448,cover=5.91711426"
[419] "30:[f27<0.119999997] yes=45,no=46,missing=45,gain=13.1309738,cover=13.7064667"
[420] "45:leaf=-0.286996275,cover=11.43013"
[421] "46:leaf=0.394486934,cover=2.27633691"
[422] "18:[f5<0.25999999] yes=31,no=32,missing=31,gain=4.20750427,cover=16.4457684"
[423] "31:[f54<4.90450001] yes=47,no=48,missing=47,gain=1.99563026,cover=15.2083368"
[424] "47:leaf=-0.34628281,cover=13.7720242"
[425] "48:leaf=0.0144406641,cover=1.43631184"
[426] "32:leaf=0.163781703,cover=1.23743236"
[427] "4:[f10<0.425000012] yes=9,no=10,missing=9,gain=5.39309692,cover=59.0693779"
[428] "9:[f15<0.720000029] yes=19,no=20,missing=19,gain=2.10302353,cover=57.7884483"
[429] "19:[f56<1921] yes=33,no=34,missing=33,gain=0.709747314,cover=56.6808739"
[430] "33:leaf=-0.324580252,cover=55.6572037"
[431] "34:leaf=-0.0343795009,cover=1.02366769"
[432] "20:leaf=0.0543748029,cover=1.10757434"
[433] "10:leaf=0.198634997,cover=1.28092957"
[434] "2:[f24<0.264999986] yes=5,no=6,missing=5,gain=22.405302,cover=89.2755585"
[435] "5:[f45<0.49000001] yes=11,no=12,missing=11,gain=5.52398682,cover=76.0112381"
[436] "11:[f41<0.189999998] yes=21,no=22,missing=21,gain=4.7574501,cover=74.4785995"
[437] "21:[f26<0.0500000007] yes=35,no=36,missing=35,gain=3.54431152,cover=72.6263123"
[438] "35:[f20<0.00999999978] yes=49,no=50,missing=49,gain=2.25080109,cover=70.9834747"
[439] "49:leaf=0.114417255,cover=9.99465179"
[440] "50:leaf=0.284638941,cover=60.9888268"
[441] "36:leaf=-0.124395378,cover=1.64283085"
[442] "22:leaf=-0.167952225,cover=1.85229087"
[443] "12:leaf=-0.233237267,cover=1.53264046"
[444] "6:[f51<0.833500028] yes=13,no=14,missing=13,gain=3.10369158,cover=13.2643147"
[445] "13:[f22<0.0450000018] yes=23,no=24,missing=23,gain=1.86696482,cover=12.1343861"
[446] "23:[f44<0.480000019] yes=37,no=38,missing=37,gain=0.171444893,cover=8.5934906"
[447] "37:leaf=-0.316979975,cover=7.59212828"
[448] "38:leaf=-0.0731857121,cover=1.00136268"
[449] "24:[f2<0.0250000004] yes=39,no=40,missing=39,gain=2.72871637,cover=3.54089475"
[450] "39:leaf=0.234009251,cover=1.18300354"
[451] "40:leaf=-0.197658524,cover=2.35789132"
[452] "14:leaf=0.181939662,cover=1.12992871"
[453] "booster[7]"
[454] "0:[f26<0.00499999989] yes=1,no=2,missing=1,gain=43.3461151,cover=306.708038"
[455] "1:[f4<0.0450000018] yes=3,no=4,missing=3,gain=32.7159996,cover=264.913666"
[456] "3:[f22<0.254999995] yes=7,no=8,missing=7,gain=17.6426601,cover=165.163681"
[457] "7:[f15<0.814999998] yes=11,no=12,missing=11,gain=13.256752,cover=155.345978"
[458] "11:[f23<0.405000001] yes=19,no=20,missing=19,gain=11.5676136,cover=141.709457"
[459] "19:[f44<0.394999981] yes=29,no=30,missing=29,gain=7.57562828,cover=134.063232"
[460] "29:leaf=-0.110394388,cover=109.562805"
[461] "30:leaf=-0.294580996,cover=24.5004292"
[462] "20:[f44<0.435000002] yes=31,no=32,missing=31,gain=4.10295439,cover=7.64622164"
[463] "31:leaf=0.298579365,cover=6.62503147"
[464] "32:leaf=-0.208337873,cover=1.02119005"
[465] "12:[f45<0.50999999] yes=21,no=22,missing=21,gain=3.82143307,cover=13.6365242"
[466] "21:[f17<0.954999983] yes=33,no=34,missing=33,gain=2.7998724,cover=12.457202"
[467] "33:leaf=0.280590177,cover=10.7228727"
[468] "34:leaf=-0.0871500075,cover=1.73432946"
[469] "22:leaf=-0.219149202,cover=1.179322"
[470] "8:[f44<0.0450000018] yes=13,no=14,missing=13,gain=1.48372173,cover=9.81770134"
[471] "13:leaf=0.336797714,cover=8.69988823"
[472] "14:leaf=-0.0152030867,cover=1.11781287"
[473] "4:[f24<0.0949999988] yes=9,no=10,missing=9,gain=20.5788116,cover=99.7499847"
[474] "9:[f41<0.464999974] yes=15,no=16,missing=15,gain=10.9892845,cover=83.49543"
[475] "15:[f45<0.140000001] yes=23,no=24,missing=23,gain=7.62929153,cover=80.2942657"
[476] "23:[f20<0.00999999978] yes=35,no=36,missing=35,gain=5.57199097,cover=73.1627197"
[477] "35:leaf=0.0576629862,cover=11.513649"
[478] "36:leaf=0.290555686,cover=61.6490746"
[479] "24:[f27<0.229999989] yes=37,no=38,missing=37,gain=6.9703064,cover=7.13154221"
[480] "37:leaf=-0.21502237,cover=5.66721153"
[481] "38:leaf=0.377057761,cover=1.46433043"
[482] "16:leaf=-0.281235307,cover=3.20116544"
[483] "10:[f54<2.77250004] yes=17,no=18,missing=17,gain=5.73563433,cover=16.2545567"
[484] "17:[f54<1.35000002] yes=25,no=26,missing=25,gain=0.979086876,cover=11.0901861"
[485] "25:leaf=-0.00836809166,cover=1.2086097"
[486] "26:leaf=-0.300114393,cover=9.88157654"
[487] "18:[f9<0.115000002] yes=27,no=28,missing=27,gain=4.18467999,cover=5.16437054"
[488] "27:leaf=-0.205980629,cover=1.84679806"
[489] "28:[f11<0.735000014] yes=39,no=40,missing=39,gain=0.174739838,cover=3.31757236"
[490] "39:leaf=0.316601247,cover=2.09378457"
[491] "40:leaf=0.0780379772,cover=1.22378778"
[492] "2:[f5<0.909999967] yes=5,no=6,missing=5,gain=0.749893188,cover=41.7943802"
[493] "5:leaf=-0.322382092,cover=40.772789"
[494] "6:leaf=-0.0297514629,cover=1.02159095"
[495] "booster[8]"
[496] "0:[f26<0.00499999989] yes=1,no=2,missing=1,gain=31.1022186,cover=270.563904"
[497] "1:[f24<0.114999995] yes=3,no=4,missing=3,gain=24.196312,cover=238.16745"
[498] "3:[f16<0.0649999976] yes=7,no=8,missing=7,gain=22.128231,cover=201.972809"
[499] "7:[f54<2.2954998] yes=11,no=12,missing=11,gain=18.1476326,cover=166.129288"
[500] "11:[f44<0.485000014] yes=17,no=18,missing=17,gain=10.5051184,cover=86.4226074"
[501] "17:[f7<0.314999998] yes=27,no=28,missing=27,gain=9.8603878,cover=70.5598373"
[502] "27:leaf=-0.0743602812,cover=65.2792206"
[503] "28:leaf=0.319306612,cover=5.28061581"
[504] "18:[f11<1.8499999] yes=29,no=30,missing=29,gain=0.319725037,cover=15.862771"
[505] "29:leaf=-0.317040145,cover=14.8386307"
[506] "30:leaf=-0.0620187633,cover=1.02414024"
[507] "12:[f45<0.49000001] yes=19,no=20,missing=19,gain=14.4325619,cover=79.7066879"
[508] "19:[f48<0.932500005] yes=31,no=32,missing=31,gain=10.0566311,cover=71.7712784"
[509] "31:leaf=0.170710787,cover=69.3634491"
[510] "32:leaf=-0.363244951,cover=2.40782785"
[511] "20:[f18<0.00499999989] yes=33,no=34,missing=33,gain=0.787613392,cover=7.93540812"
[512] "33:leaf=-0.0466354489,cover=1.75518382"
[513] "34:leaf=-0.302724719,cover=6.18022442"
[514] "8:[f41<0.00999999978] yes=13,no=14,missing=13,gain=4.30820084,cover=35.8435173"
[515] "13:[f11<1.26999998] yes=21,no=22,missing=21,gain=2.0457077,cover=33.9746475"
[516] "21:[f56<56.5] yes=35,no=36,missing=35,gain=0.68812561,cover=29.1106758"
[517] "35:leaf=0.0929691866,cover=2.18016863"
[518] "36:leaf=0.32759732,cover=26.9305077"
[519] "22:[f18<2.09500003] yes=37,no=38,missing=37,gain=3.53364253,cover=4.8639698"
[520] "37:leaf=-0.191273302,cover=1.7996372"
[521] "38:leaf=0.250708193,cover=3.06433272"
[522] "14:leaf=-0.129565656,cover=1.86886978"
[523] "4:[f10<0.425000012] yes=9,no=10,missing=9,gain=4.7523632,cover=36.1946411"
[524] "9:[f56<907] yes=15,no=16,missing=15,gain=4.18074226,cover=34.647213"
[525] "15:[f15<0.610000014] yes=23,no=24,missing=23,gain=2.04537201,cover=31.6378593"
[526] "23:[f52<0.134499997] yes=39,no=40,missing=39,gain=1.88686562,cover=30.4840374"
[527] "39:leaf=-0.298568755,cover=29.4164391"
[528] "40:leaf=0.0595468432,cover=1.06759715"
[529] "24:leaf=0.0709220693,cover=1.15382385"
[530] "16:[f29<0.0250000004] yes=25,no=26,missing=25,gain=2.27756667,cover=3.00935102"
[531] "25:leaf=0.276134551,cover=1.39485168"
[532] "26:leaf=-0.133587956,cover=1.61449945"
[533] "10:leaf=0.207471207,cover=1.54743028"
[534] "2:[f5<0.420000017] yes=5,no=6,missing=5,gain=0.7942276,cover=32.3964691"
[535] "5:leaf=-0.31242615,cover=30.5620804"
[536] "6:leaf=-0.0649266541,cover=1.8343873"
[537] "booster[9]"
[538] "0:[f26<0.00499999989] yes=1,no=2,missing=1,gain=22.0823364,cover=240.94101"
[539] "1:[f22<0.25999999] yes=3,no=4,missing=3,gain=16.9999084,cover=215.743134"
[540] "3:[f36<0.0649999976] yes=7,no=8,missing=7,gain=14.1344986,cover=196.594727"
[541] "7:[f56<103.5] yes=11,no=12,missing=11,gain=13.5768642,cover=167.080521"
[542] "11:[f18<3.03999996] yes=17,no=18,missing=17,gain=10.1767101,cover=106.739014"
[543] "17:[f20<1.255] yes=25,no=26,missing=25,gain=5.80861664,cover=76.6054993"
[544] "25:leaf=-0.145126805,cover=64.0847778"
[545] "26:leaf=0.0733757615,cover=12.5207224"
[546] "18:[f44<0.774999976] yes=27,no=28,missing=27,gain=5.86016941,cover=30.1335125"
[547] "27:leaf=0.156630591,cover=24.6110077"
[548] "28:leaf=-0.164484024,cover=5.52250385"
[549] "12:[f48<0.965499997] yes=19,no=20,missing=19,gain=6.49913502,cover=60.3415108"
[550] "19:[f54<1.8375001] yes=29,no=30,missing=29,gain=7.47935104,cover=58.2962418"
[551] "29:leaf=-0.0883502513,cover=9.63896465"
[552] "30:leaf=0.193101317,cover=48.65728"
[553] "20:leaf=-0.309444785,cover=2.04526734"
[554] "8:[f15<0.845000029] yes=13,no=14,missing=13,gain=4.00631714,cover=29.5142117"
[555] "13:[f10<0.444999993] yes=21,no=22,missing=21,gain=4.10984612,cover=28.1857758"
[556] "21:[f48<0.167500004] yes=31,no=32,missing=31,gain=4.42033195,cover=27.1370716"
[557] "31:leaf=-0.299456358,cover=24.1845531"
[558] "32:leaf=0.0684102848,cover=2.95251989"
[559] "22:leaf=0.212186038,cover=1.04870296"
[560] "14:leaf=0.197972789,cover=1.32843637"
[561] "4:[f10<0.469999999] yes=9,no=10,missing=9,gain=1.53589821,cover=19.1483936"
[562] "9:[f35<0.0450000018] yes=15,no=16,missing=15,gain=0.620206833,cover=17.3715611"
[563] "15:[f55<8.5] yes=23,no=24,missing=23,gain=0.0140991211,cover=16.3411255"
[564] "23:leaf=0.0918928608,cover=1.03812611"
[565] "24:leaf=0.315639049,cover=15.3030005"
[566] "16:leaf=0.0322851203,cover=1.03043532"
[567] "10:leaf=0.00112847611,cover=1.77683258"
[568] "2:[f16<0.355000019] yes=5,no=6,missing=5,gain=0.899616241,cover=25.1978779"
[569] "5:leaf=-0.300720781,cover=23.5431786"
[570] "6:leaf=-0.0424691215,cover=1.65469944"
[571] "booster[10]"
[572] "0:[f24<0.114999995] yes=1,no=2,missing=1,gain=17.5429153,cover=220.404175"
[573] "1:[f55<9.5] yes=3,no=4,missing=3,gain=13.8000927,cover=185.527344"
[574] "3:[f51<0.983999968] yes=7,no=8,missing=7,gain=7.20861864,cover=65.780632"
[575] "7:[f17<0.36500001] yes=13,no=14,missing=13,gain=6.7215395,cover=59.5954742"
[576] "13:[f6<0.889999986] yes=25,no=26,missing=25,gain=5.18266106,cover=53.2444534"
[577] "25:[f15<0.544999957] yes=41,no=42,missing=41,gain=3.59808922,cover=51.445694"
[578] "41:leaf=-0.20680733,cover=46.0377884"
[579] "42:leaf=0.0443140864,cover=5.40790462"
[580] "26:leaf=0.250227898,cover=1.79875779"
[581] "14:[f55<4.5] yes=27,no=28,missing=27,gain=1.99450815,cover=6.35102081"
[582] "27:[f4<0.964999974] yes=43,no=44,missing=43,gain=2.06981993,cover=3.2758801"
[583] "43:leaf=-0.18929705,cover=1.85639453"
[584] "44:leaf=0.18813771,cover=1.41948545"
[585] "28:[f51<0.191] yes=45,no=46,missing=45,gain=0.839158773,cover=3.07514095"
[586] "45:leaf=0.36958006,cover=2.02870345"
[587] "46:leaf=0.0280240178,cover=1.04643738"
[588] "8:[f44<1.245] yes=15,no=16,missing=15,gain=3.37973809,cover=6.18516159"
[589] "15:[f54<1.70799994] yes=29,no=30,missing=29,gain=0.253184795,cover=5.1409812"
[590] "29:[f2<0.36500001] yes=47,no=48,missing=47,gain=0.615327835,cover=4.036901"
[591] "47:leaf=0.383786768,cover=2.83164954"
[592] "48:leaf=0.0660719648,cover=1.20525169"
[593] "30:leaf=0.0696772337,cover=1.10407972"
[594] "16:leaf=-0.181759238,cover=1.04418075"
[595] "4:[f36<0.199999988] yes=9,no=10,missing=9,gain=10.2572985,cover=119.746712"
[596] "9:[f4<0.00999999978] yes=17,no=18,missing=17,gain=7.14442635,cover=105.532089"
[597] "17:[f55<42.5] yes=31,no=32,missing=31,gain=7.53953981,cover=62.5791245"
[598] "31:[f55<32.5] yes=49,no=50,missing=49,gain=5.66855145,cover=47.182663"
[599] "49:leaf=0.0627230853,cover=42.5116119"
[600] "50:leaf=0.384769619,cover=4.67104959"
[601] "32:[f52<0.050999999] yes=51,no=52,missing=51,gain=7.56710148,cover=15.3964605"
[602] "51:leaf=-0.234392911,cover=12.4671783"
[603] "52:leaf=0.244616106,cover=2.92928195"
[604] "18:[f10<0.474999994] yes=33,no=34,missing=33,gain=5.149683,cover=42.9529648"
[605] "33:[f41<0.414999992] yes=53,no=54,missing=53,gain=2.54151726,cover=40.3792839"
[606] "53:leaf=0.241076127,cover=39.3076019"
[607] "54:leaf=-0.135561496,cover=1.07168186"
[608] "34:[f55<33.5] yes=55,no=56,missing=55,gain=5.86164427,cover=2.57368088"
[609] "55:leaf=-0.503028333,cover=1.10095668"
[610] "56:leaf=0.191761196,cover=1.4727242"
[611] "10:[f54<4.52400017] yes=19,no=20,missing=19,gain=3.09484863,cover=14.2146244"
[612] "19:[f55<30.5] yes=35,no=36,missing=35,gain=0.993920326,cover=10.725976"
[613] "35:[f15<0.280000001] yes=57,no=58,missing=57,gain=1.79341519,cover=6.92743635"
[614] "57:leaf=-0.193309516,cover=5.87282848"
[615] "58:leaf=0.140889689,cover=1.05460799"
[616] "36:leaf=-0.346188217,cover=3.79853988"
[617] "20:[f2<0.485000014] yes=37,no=38,missing=37,gain=1.37321091,cover=3.48864818"
[618] "37:[f36<0.940000057] yes=59,no=60,missing=59,gain=0.0501270294,cover=2.45719314"
[619] "59:leaf=0.0491509996,cover=1.01627505"
[620] "60:leaf=0.211388573,cover=1.44091809"
[621] "38:leaf=-0.13850145,cover=1.03145504"
[622] "2:[f10<0.425000012] yes=5,no=6,missing=5,gain=3.1765213,cover=34.8768311"
[623] "5:[f22<0.0149999997] yes=11,no=12,missing=11,gain=2.51783752,cover=33.2335167"
[624] "11:[f15<0.695000052] yes=21,no=22,missing=21,gain=1.72016525,cover=30.2428207"
[625] "21:[f19<0.174999997] yes=39,no=40,missing=39,gain=1.10501862,cover=29.1672192"
[626] "39:[f32<1.01999998] yes=61,no=62,missing=61,gain=0.62172699,cover=28.1556301"
[627] "61:leaf=-0.292788357,cover=26.9310799"
[628] "62:leaf=-0.0381427631,cover=1.22454882"
[629] "40:leaf=0.0184148885,cover=1.01159048"
[630] "22:leaf=0.0637860149,cover=1.07560086"
[631] "12:[f48<0.0820000023] yes=23,no=24,missing=23,gain=2.15000629,cover=2.99069667"
[632] "23:leaf=-0.144530579,cover=1.97546268"
[633] "24:leaf=0.257186294,cover=1.01523399"
[634] "6:leaf=0.13297233,cover=1.64331424"
[635] "booster[11]"
[636] "0:[f26<0.00499999989] yes=1,no=2,missing=1,gain=13.7812099,cover=199.017822"
[637] "1:[f41<0.454999983] yes=3,no=4,missing=3,gain=11.8233223,cover=182.173935"
[638] "3:[f45<0.105000004] yes=7,no=8,missing=7,gain=10.5766716,cover=173.32933"
[639] "7:[f25<0.0700000003] yes=11,no=12,missing=11,gain=10.299345,cover=152.560928"
[640] "11:[f44<0.474999994] yes=15,no=16,missing=15,gain=7.25176239,cover=137.885178"
[641] "15:[f18<1.80499995] yes=23,no=24,missing=23,gain=7.01900959,cover=120.960388"
[642] "23:leaf=0.0393665731,cover=67.4758453"
[643] "24:leaf=0.184893146,cover=53.484539"
[644] "16:[f52<0.145999998] yes=25,no=26,missing=25,gain=3.36298633,cover=16.9247913"
[645] "25:leaf=-0.171760216,cover=12.9743166"
[646] "26:leaf=0.120509759,cover=3.95047402"
[647] "12:[f44<0.344999999] yes=17,no=18,missing=17,gain=3.39289236,cover=14.6757536"
[648] "17:[f4<0.769999981] yes=27,no=28,missing=27,gain=2.82785511,cover=12.7402678"
[649] "27:leaf=-0.28815788,cover=10.8438568"
[650] "28:leaf=0.0738757178,cover=1.89641106"
[651] "18:leaf=0.139417067,cover=1.9354856"
[652] "8:[f27<0.229999989] yes=13,no=14,missing=13,gain=8.44047356,cover=20.7683983"
[653] "13:[f4<0.514999986] yes=19,no=20,missing=19,gain=1.73012161,cover=17.9236317"
[654] "19:[f11<1.37] yes=29,no=30,missing=29,gain=0.699894905,cover=16.2264633"
[655] "29:leaf=-0.290573239,cover=15.190382"
[656] "30:leaf=-0.0178008918,cover=1.03608167"
[657] "20:leaf=0.0273585338,cover=1.69716763"
[658] "14:[f54<2.46749997] yes=21,no=22,missing=21,gain=0.268998623,cover=2.84476709"
[659] "21:leaf=0.306391418,cover=1.73843634"
[660] "22:leaf=0.0575627163,cover=1.10633087"
[661] "4:leaf=-0.312296212,cover=8.84460735"
[662] "2:[f5<0.420000017] yes=5,no=6,missing=5,gain=0.739887238,cover=16.8438854"
[663] "5:[f8<0.639999986] yes=9,no=10,missing=9,gain=0.0692911148,cover=15.544035"
[664] "9:leaf=-0.294586927,cover=14.5057545"
[665] "10:leaf=-0.0795330107,cover=1.03828001"
[666] "6:leaf=-0.0274123922,cover=1.29984963"
[667] "booster[12]"
[668] "0:[f23<0.00999999978] yes=1,no=2,missing=1,gain=10.1863613,cover=186.077667"
[669] "1:[f51<0.457499981] yes=3,no=4,missing=3,gain=9.41360664,cover=165.868393"
[670] "3:[f26<0.00499999989] yes=7,no=8,missing=7,gain=6.39834213,cover=137.384811"
[671] "7:[f5<0.314999998] yes=13,no=14,missing=13,gain=4.94257927,cover=125.628494"
[672] "13:[f36<0.0850000009] yes=23,no=24,missing=23,gain=4.32857752,cover=116.574181"
[673] "23:[f56<468] yes=33,no=34,missing=33,gain=5.21480513,cover=100.270546"
[674] "33:leaf=-0.0613245405,cover=95.1909409"
[675] "34:leaf=0.225769475,cover=5.07960367"
[676] "24:[f48<0.169] yes=35,no=36,missing=35,gain=1.78550148,cover=16.3036327"
[677] "35:leaf=-0.241939709,cover=14.4326754"
[678] "36:leaf=0.0466570929,cover=1.8709563"
[679] "14:[f54<1.94350004] yes=25,no=26,missing=25,gain=5.36678314,cover=9.05431747"
[680] "25:[f5<0.465000004] yes=37,no=38,missing=37,gain=0.249476135,cover=3.29282236"
[681] "37:leaf=-0.00989749562,cover=1.25385976"
[682] "38:leaf=-0.177458599,cover=2.0389626"
[683] "26:[f11<0.625] yes=39,no=40,missing=39,gain=0.497808933,cover=5.76149559"
[684] "39:leaf=0.344781607,cover=4.68885946"
[685] "40:leaf=0.0556606986,cover=1.07263613"
[686] "8:leaf=-0.276147693,cover=11.756321"
[687] "4:[f56<77] yes=9,no=10,missing=9,gain=3.77010345,cover=28.4835758"
[688] "9:[f11<1.58500004] yes=15,no=16,missing=15,gain=3.59933352,cover=17.9485111"
[689] "15:[f44<1.2249999] yes=27,no=28,missing=27,gain=1.46250546,cover=15.5976229"
[690] "27:[f51<0.824999988] yes=41,no=42,missing=41,gain=1.40521336,cover=14.1960363"
[691] "41:leaf=0.00370711647,cover=6.30389547"
[692] "42:leaf=0.189467996,cover=7.89214039"
[693] "28:leaf=-0.14529486,cover=1.40158653"
[694] "16:leaf=-0.259092182,cover=2.35088944"
[695] "10:[f15<1.42000008] yes=17,no=18,missing=17,gain=0.631894588,cover=10.5350647"
[696] "17:[f36<0.189999998] yes=29,no=30,missing=29,gain=0.317004204,cover=9.51429272"
[697] "29:leaf=0.288322985,cover=8.4684906"
[698] "30:leaf=0.0508493036,cover=1.04580164"
[699] "18:leaf=0.011006617,cover=1.02077174"
[700] "2:[f44<0.444999993] yes=5,no=6,missing=5,gain=2.77604294,cover=20.20928"
[701] "5:[f49<0.20449999] yes=11,no=12,missing=11,gain=3.14390182,cover=18.5205574"
[702] "11:[f45<0.109999999] yes=19,no=20,missing=19,gain=1.88195896,cover=15.699194"
[703] "19:[f18<0.354999989] yes=31,no=32,missing=31,gain=0.111322403,cover=14.6858149"
[704] "31:leaf=0.0934873149,cover=1.379794"
[705] "32:leaf=0.302451372,cover=13.3060207"
[706] "20:leaf=-0.0728443414,cover=1.01337957"
[707] "12:[f8<0.335000008] yes=21,no=22,missing=21,gain=1.47214508,cover=2.82136393"
[708] "21:leaf=-0.190044224,cover=1.8024528"
[709] "22:leaf=0.149605513,cover=1.01891112"
[710] "6:leaf=-0.130987182,cover=1.6887219"
[711] "booster[13]"
[712] "0:[f22<0.25999999] yes=1,no=2,missing=1,gain=8.02625465,cover=173.723999"
[713] "1:[f26<0.00499999989] yes=3,no=4,missing=3,gain=6.85149479,cover=162.500214"
[714] "3:[f41<0.454999983] yes=7,no=8,missing=7,gain=6.8023777,cover=152.18367"
[715] "7:[f4<0.375] yes=13,no=14,missing=13,gain=5.82044888,cover=145.642258"
[716] "13:[f2<0.305000007] yes=15,no=16,missing=15,gain=4.37450504,cover=115.320343"
[717] "15:[f45<1.80499995] yes=19,no=20,missing=19,gain=4.06735849,cover=89.3339081"
[718] "19:leaf=0.0183024351,cover=85.5199966"
[719] "20:leaf=-0.26502797,cover=3.81390786"
[720] "16:[f15<0.665000021] yes=21,no=22,missing=21,gain=4.05561256,cover=25.9864349"
[721] "21:leaf=-0.180954456,cover=22.4561958"
[722] "22:leaf=0.136406988,cover=3.53023863"
[723] "14:[f11<0.814999998] yes=17,no=18,missing=17,gain=3.21179581,cover=30.3219109"
[724] "17:[f43<0.0250000004] yes=23,no=24,missing=23,gain=2.54590178,cover=22.67803"
[725] "23:leaf=0.197430551,cover=21.4198494"
[726] "24:leaf=-0.156857535,cover=1.25817955"
[727] "18:[f20<0.454999983] yes=25,no=26,missing=25,gain=3.7567482,cover=7.64388132"
[728] "25:leaf=-0.304586172,cover=2.2583158"
[729] "26:leaf=0.0924226195,cover=5.38556528"
[730] "8:leaf=-0.289795995,cover=6.54142284"
[731] "4:[f8<0.360000014] yes=9,no=10,missing=9,gain=0.519197464,cover=10.3165398"
[732] "9:leaf=-0.27879265,cover=8.93866062"
[733] "10:leaf=-0.0437295064,cover=1.37787879"
[734] "2:[f10<0.469999999] yes=5,no=6,missing=5,gain=1.66400862,cover=11.2237797"
[735] "5:[f36<0.0850000009] yes=11,no=12,missing=11,gain=0.360965729,cover=9.791255"
[736] "11:leaf=0.289283454,cover=8.23530006"
[737] "12:leaf=0.0699158236,cover=1.55595493"
[738] "6:leaf=-0.0499542169,cover=1.4325254"
[739] "booster[14]"
[740] "0:[f24<0.114999995] yes=1,no=2,missing=1,gain=6.71800041,cover=165.518005"
[741] "1:[f16<0.0649999976] yes=3,no=4,missing=3,gain=7.23904896,cover=143.869003"
[742] "3:[f35<0.335000008] yes=7,no=8,missing=7,gain=8.55298328,cover=124.697037"
[743] "7:[f23<0.0199999996] yes=15,no=16,missing=15,gain=5.60011482,cover=121.071152"
[744] "15:[f44<0.0399999991] yes=25,no=26,missing=25,gain=4.69654083,cover=111.305504"
[745] "25:[f5<0.519999981] yes=35,no=36,missing=35,gain=3.79500413,cover=89.6942978"
[746] "35:leaf=-0.0318490863,cover=85.4863358"
[747] "36:leaf=0.231950089,cover=4.20796776"
[748] "26:[f54<3.74100018] yes=37,no=38,missing=37,gain=3.89009619,cover=21.6112022"
[749] "37:leaf=-0.229298607,cover=17.5326805"
[750] "38:leaf=0.078592509,cover=4.07852077"
[751] "16:[f45<0.140000001] yes=27,no=28,missing=27,gain=1.59629655,cover=9.76565361"
[752] "27:[f56<63.5] yes=39,no=40,missing=39,gain=1.22391176,cover=8.62063503"
[753] "39:leaf=0.024786802,cover=2.4136734"
[754] "40:leaf=0.282849133,cover=6.20696163"
[755] "28:leaf=-0.0932549536,cover=1.14501846"
[756] "8:[f11<0.199999988] yes=17,no=18,missing=17,gain=0.46661377,cover=3.62588286"
[757] "17:leaf=0.134502321,cover=1.61579025"
[758] "18:leaf=0.478123814,cover=2.0100925"
[759] "4:[f41<0.00499999989] yes=9,no=10,missing=9,gain=1.85087585,cover=19.1719627"
[760] "9:[f20<3.375] yes=19,no=20,missing=19,gain=1.74507236,cover=18.0012875"
[761] "19:[f35<0.140000001] yes=29,no=30,missing=29,gain=0.749493599,cover=16.9359608"
[762] "29:[f11<1.25999999] yes=41,no=42,missing=41,gain=0.662388802,cover=15.9182434"
[763] "41:leaf=0.270714611,cover=13.1513128"
[764] "42:leaf=0.0738372058,cover=2.76693082"
[765] "30:leaf=-0.0114546716,cover=1.01771808"
[766] "20:leaf=-0.0981129035,cover=1.06532538"
[767] "10:leaf=-0.114052787,cover=1.17067683"
[768] "2:[f9<0.145000011] yes=5,no=6,missing=5,gain=2.04887438,cover=21.6490116"
[769] "5:[f10<0.100000001] yes=11,no=12,missing=11,gain=1.01353359,cover=15.0209208"
[770] "11:[f32<0.664999962] yes=21,no=22,missing=21,gain=0.62698555,cover=13.5483122"
[771] "21:[f43<0.375] yes=31,no=32,missing=31,gain=0.726372719,cover=12.5143003"
[772] "31:leaf=-0.28136307,cover=11.5079899"
[773] "32:leaf=-0.00810715649,cover=1.00631082"
[774] "22:leaf=-0.0095815789,cover=1.03401089"
[775] "12:leaf=0.0101625537,cover=1.47260928"
[776] "6:[f54<2.61450005] yes=13,no=14,missing=13,gain=2.71890402,cover=6.6280899"
[777] "13:leaf=-0.229981259,cover=2.31688809"
[778] "14:[f18<1.71000004] yes=23,no=24,missing=23,gain=0.944814801,cover=4.31120157"
[779] "23:[f51<0.0274999999] yes=33,no=34,missing=33,gain=1.15253925,cover=2.82814884"
[780] "33:leaf=-0.13848342,cover=1.57280385"
[781] "34:leaf=0.155304372,cover=1.25534511"
[782] "24:leaf=0.25147301,cover=1.48305261"
[783] "booster[15]"
[784] "0:[f41<0.454999983] yes=1,no=2,missing=1,gain=5.0738759,cover=156.042694"
[785] "1:[f25<0.0649999976] yes=3,no=4,missing=3,gain=4.80138779,cover=150.784286"
[786] "3:[f56<16.5] yes=5,no=6,missing=5,gain=5.35411978,cover=137.973404"
[787] "5:[f51<1.74650002] yes=9,no=10,missing=9,gain=5.76440334,cover=21.8589535"
[788] "9:[f18<4.39000034] yes=15,no=16,missing=15,gain=3.84801674,cover=19.4910126"
[789] "15:[f15<0.814999998] yes=25,no=26,missing=25,gain=1.66663742,cover=15.4994984"
[790] "25:leaf=-0.258210272,cover=14.4321508"
[791] "26:leaf=0.073717609,cover=1.06734741"
[792] "16:[f54<1.52749991] yes=27,no=28,missing=27,gain=0.623910367,cover=3.99151373"
[793] "27:leaf=0.138178736,cover=2.98979044"
[794] "28:leaf=-0.0765335411,cover=1.00172329"
[795] "10:[f56<10.5] yes=17,no=18,missing=17,gain=0.0821959972,cover=2.36794138"
[796] "17:leaf=0.313257515,cover=1.27633476"
[797] "18:leaf=0.078973569,cover=1.0916065"
[798] "6:[f44<0.36500001] yes=11,no=12,missing=11,gain=4.27094841,cover=116.114449"
[799] "11:[f36<0.0649999976] yes=19,no=20,missing=19,gain=4.0611968,cover=97.8998566"
[800] "19:[f48<1.13400006] yes=29,no=30,missing=29,gain=3.86317635,cover=85.4058533"
[801] "29:leaf=0.105491631,cover=83.6969833"
[802] "30:leaf=-0.263108552,cover=1.70886886"
[803] "20:[f36<0.75] yes=31,no=32,missing=31,gain=2.31638718,cover=12.4940052"
[804] "31:leaf=-0.173918635,cover=7.89483452"
[805] "32:leaf=0.0761598796,cover=4.59917021"
[806] "12:[f15<0.804999948] yes=21,no=22,missing=21,gain=2.87368441,cover=18.2145882"
[807] "21:[f52<0.141000003] yes=33,no=34,missing=33,gain=2.51154304,cover=15.4253778"
[808] "33:leaf=-0.193858474,cover=12.1385393"
[809] "34:leaf=0.0803262815,cover=3.28683901"
[810] "22:[f18<3.71000004] yes=35,no=36,missing=35,gain=0.114027023,cover=2.78921008"
[811] "35:leaf=0.195385098,cover=1.72583675"
[812] "36:leaf=0.0345923938,cover=1.06337333"
[813] "4:[f20<0.164999992] yes=7,no=8,missing=7,gain=1.86203861,cover=12.8108873"
[814] "7:leaf=-0.268327445,cover=6.33608103"
[815] "8:[f44<0.344999999] yes=13,no=14,missing=13,gain=2.15118313,cover=6.47480631"
[816] "13:[f4<0.74000001] yes=23,no=24,missing=23,gain=2.24266481,cover=5.11129284"
[817] "23:leaf=-0.236360133,cover=3.70198989"
[818] "24:leaf=0.132130563,cover=1.40930307"
[819] "14:leaf=0.208511218,cover=1.36351347"
[820] "2:leaf=-0.271178573,cover=5.25841045"
[821] "booster[16]"
[822] "0:[f26<0.00499999989] yes=1,no=2,missing=1,gain=4.32476139,cover=148.688278"
[823] "1:[f38<0.774999976] yes=3,no=4,missing=3,gain=4.16591024,cover=141.694931"
[824] "3:[f40<0.00999999978] yes=7,no=8,missing=7,gain=4.0496645,cover=138.029724"
[825] "7:[f41<0.454999983] yes=9,no=10,missing=9,gain=3.62811303,cover=134.502075"
[826] "9:[f49<0.611999989] yes=11,no=12,missing=11,gain=3.33116436,cover=131.118942"
[827] "11:[f6<0.625] yes=13,no=14,missing=13,gain=3.59748244,cover=126.188995"
[828] "13:leaf=0.0281520579,cover=118.29493"
[829] "14:leaf=0.226473153,cover=7.89406872"
[830] "12:[f49<0.921499968] yes=15,no=16,missing=15,gain=0.578398466,cover=4.92994976"
[831] "15:leaf=-0.234274298,cover=3.77466774"
[832] "16:leaf=0.00122965092,cover=1.1552819"
[833] "10:leaf=-0.245445445,cover=3.38312674"
[834] "8:leaf=-0.265028864,cover=3.52764773"
[835] "4:leaf=-0.273969918,cover=3.66520619"
[836] "2:[f51<0.240500003] yes=5,no=6,missing=5,gain=0.654488564,cover=6.99334574"
[837] "5:leaf=-0.257157981,cover=5.83840275"
[838] "6:leaf=-0.00686721876,cover=1.15494287"
[839] "booster[17]"
[840] "0:[f28<0.125] yes=1,no=2,missing=1,gain=3.88810325,cover=144.346497"
[841] "1:[f45<0.0850000009] yes=3,no=4,missing=3,gain=3.63139892,cover=139.96431"
[842] "3:[f43<0.474999994] yes=7,no=8,missing=7,gain=4.58339071,cover=125.705933"
[843] "7:[f5<0.534999967] yes=11,no=12,missing=11,gain=3.11711168,cover=121.737343"
[844] "11:[f56<883] yes=15,no=16,missing=15,gain=2.81965232,cover=115.53019"
[845] "15:[f24<0.715000033] yes=21,no=22,missing=21,gain=3.25732517,cover=110.65284"
[846] "21:leaf=0.02119717,cover=103.145889"
[847] "22:leaf=-0.171887428,cover=7.50694942"
[848] "16:[f36<0.125] yes=23,no=24,missing=23,gain=0.285058737,cover=4.87735081"
[849] "23:leaf=0.259458184,cover=3.51286936"
[850] "24:leaf=0.0516305193,cover=1.36448169"
[851] "12:[f55<11.5] yes=17,no=18,missing=17,gain=1.48474479,cover=6.2071538"
[852] "17:leaf=-0.0318086483,cover=1.43265665"
[853] "18:leaf=0.289368749,cover=4.77449751"
[854] "8:leaf=-0.264438003,cover=3.96858716"
[855] "4:[f13<0.114999995] yes=9,no=10,missing=9,gain=3.49629092,cover=14.2583742"
[856] "9:[f9<0.754999995] yes=13,no=14,missing=13,gain=2.71580362,cover=13.2479706"
[857] "13:[f49<0.349999994] yes=19,no=20,missing=19,gain=2.4810009,cover=11.335001"
[858] "19:[f11<0.63499999] yes=25,no=26,missing=25,gain=0.108050346,cover=9.8127718"
[859] "25:leaf=-0.311955869,cover=7.83129692"
[860] "26:leaf=-0.116577491,cover=1.98147511"
[861] "20:leaf=0.0794235468,cover=1.52222884"
[862] "14:leaf=0.106543168,cover=1.91297019"
[863] "10:leaf=0.252308458,cover=1.01040375"
[864] "2:[f49<0.327499986] yes=5,no=6,missing=5,gain=0.339761972,cover=4.38218403"
[865] "5:leaf=-0.296511322,cover=3.38179684"
[866] "6:leaf=-0.0451309606,cover=1.00038719"
[867] "booster[18]"
[868] "0:[f15<2.41000009] yes=1,no=2,missing=1,gain=3.30186725,cover=139.258911"
[869] "1:[f22<0.25999999] yes=3,no=4,missing=3,gain=3.26670146,cover=135.497925"
[870] "3:[f51<0.476999998] yes=7,no=8,missing=7,gain=2.98471236,cover=128.487976"
[871] "7:[f4<2.42500019] yes=11,no=12,missing=11,gain=2.54946613,cover=107.197174"
[872] "11:[f23<0.164999992] yes=17,no=18,missing=17,gain=2.64162803,cover=105.133827"
[873] "17:[f0<0.194999993] yes=21,no=22,missing=21,gain=3.71229219,cover=98.934227"
[874] "21:leaf=-0.045914229,cover=88.8709793"
[875] "22:leaf=-0.231297776,cover=10.0632524"
[876] "18:[f55<9.5] yes=23,no=24,missing=23,gain=1.50123191,cover=6.19959593"
[877] "23:leaf=-0.120708555,cover=1.11335468"
[878] "24:leaf=0.186733395,cover=5.08624125"
[879] "12:leaf=0.224257648,cover=2.06334519"
[880] "8:[f11<1.49000001] yes=13,no=14,missing=13,gain=2.55233622,cover=21.2908077"
[881] "13:[f56<73.5] yes=19,no=20,missing=19,gain=1.96554661,cover=18.8179436"
[882] "19:[f51<0.569499969] yes=25,no=26,missing=25,gain=0.849975526,cover=11.9265862"
[883] "25:leaf=0.175783291,cover=1.89403236"
[884] "26:leaf=-0.00804823358,cover=10.0325537"
[885] "20:[f12<0.13499999] yes=27,no=28,missing=27,gain=0.855191231,cover=6.89135742"
[886] "27:leaf=0.267498106,cover=5.84612274"
[887] "28:leaf=-0.0124788554,cover=1.04523456"
[888] "14:leaf=-0.169615522,cover=2.47286367"
[889] "4:[f8<0.409999996] yes=9,no=10,missing=9,gain=2.06705523,cover=7.00994253"
[890] "9:[f36<0.075000003] yes=15,no=16,missing=15,gain=0.141070366,cover=5.99336815"
[891] "15:leaf=0.255229771,cover=4.98373032"
[892] "16:leaf=0.0544938855,cover=1.00963795"
[893] "10:leaf=-0.133912146,cover=1.01657403"
[894] "2:[f55<13] yes=5,no=6,missing=5,gain=0.194711447,cover=3.76098347"
[895] "5:leaf=0.275199026,cover=2.73685145"
[896] "6:leaf=0.0533696264,cover=1.02413201"
[897] "booster[19]"
[898] "0:[f26<0.00499999989] yes=1,no=2,missing=1,gain=3.15486789,cover=134.354187"
[899] "1:[f38<0.774999976] yes=3,no=4,missing=3,gain=2.80961323,cover=129.107574"
[900] "3:[f38<0.465000004] yes=7,no=8,missing=7,gain=3.00633168,cover=126.272575"
[901] "7:[f28<0.125] yes=9,no=10,missing=9,gain=2.7895627,cover=124.072037"
[902] "9:[f45<1.56999993] yes=11,no=12,missing=11,gain=2.44436383,cover=121.763054"
[903] "11:[f15<1.96500003] yes=13,no=14,missing=13,gain=2.35540342,cover=118.639732"
[904] "13:leaf=0.0128842955,cover=114.104523"
[905] "14:leaf=0.21346499,cover=4.53521109"
[906] "12:leaf=-0.212770209,cover=3.12332106"
[907] "10:leaf=-0.2646074,cover=2.3089869"
[908] "8:leaf=0.301859349,cover=2.20053792"
[909] "4:leaf=-0.245892569,cover=2.83499479"
[910] "2:[f51<0.240500003] yes=5,no=6,missing=5,gain=0.314497232,cover=5.24661779"
[911] "5:leaf=-0.241563261,cover=4.22827196"
[912] "6:leaf=-0.0275273491,cover=1.01834583"
[913] "booster[20]"
[914] "0:[f16<0.514999986] yes=1,no=2,missing=1,gain=2.33803225,cover=131.969589"
[915] "1:[f43<0.394999981] yes=3,no=4,missing=3,gain=2.75657105,cover=122.643028"
[916] "3:[f44<0.474999994] yes=7,no=8,missing=7,gain=2.67366672,cover=119.027695"
[917] "7:[f18<5.84000015] yes=11,no=12,missing=11,gain=3.05871582,cover=105.092537"
[918] "11:[f11<2.41000009] yes=17,no=18,missing=17,gain=2.95502234,cover=101.399399"
[919] "17:[f18<5.33500004] yes=27,no=28,missing=27,gain=2.96775365,cover=96.8890991"
[920] "27:leaf=0.0198800378,cover=95.4291992"
[921] "28:leaf=-0.313897103,cover=1.45989835"
[922] "18:[f18<3.38499999] yes=29,no=30,missing=29,gain=0.667945385,cover=4.51030302"
[923] "29:leaf=-0.265291035,cover=3.44290471"
[924] "30:leaf=-0.00051564764,cover=1.06739831"
[925] "12:[f54<1.4375] yes=19,no=20,missing=19,gain=1.29270363,cover=3.69313622"
[926] "19:leaf=0.0270605255,cover=1.75319707"
[927] "20:leaf=0.369575143,cover=1.93993914"
[928] "8:[f54<3.74100018] yes=13,no=14,missing=13,gain=2.33518291,cover=13.9351597"
[929] "13:[f15<0.495000005] yes=21,no=22,missing=21,gain=2.00406694,cover=10.1967716"
[930] "21:[f54<1.2385] yes=31,no=32,missing=31,gain=0.792727947,cover=7.88808775"
[931] "31:leaf=-0.0237849075,cover=1.30516565"
[932] "32:leaf=-0.29690069,cover=6.58292198"
[933] "22:[f2<0.539999962] yes=33,no=34,missing=33,gain=0.403715402,cover=2.30868387"
[934] "33:leaf=-0.0639577433,cover=1.13738441"
[935] "34:leaf=0.122664608,cover=1.17129946"
[936] "14:[f56<139.5] yes=23,no=24,missing=23,gain=1.61745715,cover=3.73838806"
[937] "23:leaf=0.191688389,cover=2.318753"
[938] "24:leaf=-0.134737313,cover=1.41963506"
[939] "4:leaf=-0.242538154,cover=3.6153338"
[940] "2:[f20<3.07999992] yes=5,no=6,missing=5,gain=1.70618486,cover=9.32655716"
[941] "5:[f24<0.185000002] yes=9,no=10,missing=9,gain=1.39364266,cover=8.32484055"
[942] "9:[f54<5.69750023] yes=15,no=16,missing=15,gain=0.615968704,cover=7.00953484"
[943] "15:[f56<56.5] yes=25,no=26,missing=25,gain=0.664115429,cover=5.98046017"
[944] "25:leaf=0.0415027589,cover=1.41441309"
[945] "26:leaf=0.306649536,cover=4.56604719"
[946] "16:leaf=0.00513831573,cover=1.02907443"
[947] "10:leaf=-0.0660959855,cover=1.31530547"
[948] "6:leaf=-0.142516971,cover=1.00171685"
[949] "booster[21]"
[950] "0:[f51<1.23899996] yes=1,no=2,missing=1,gain=2.31263995,cover=128.912598"
[951] "1:[f56<16.5] yes=3,no=4,missing=3,gain=2.88747478,cover=121.870293"
[952] "3:[f18<4.39000034] yes=7,no=8,missing=7,gain=3.10642076,cover=15.7994699"
[953] "7:[f15<1.9749999] yes=13,no=14,missing=13,gain=1.68007994,cover=12.0560551"
[954] "13:[f49<0.423500001] yes=21,no=22,missing=21,gain=0.546717644,cover=10.9806156"
[955] "21:leaf=-0.253737628,cover=9.96019173"
[956] "22:leaf=-0.0118702753,cover=1.02042353"
[957] "14:leaf=0.0922419131,cover=1.07544005"
[958] "8:[f18<5.94000006] yes=15,no=16,missing=15,gain=0.133856028,cover=3.74341464"
[959] "15:leaf=0.130643055,cover=1.9791652"
[960] "16:leaf=0.0157653578,cover=1.76424944"
[961] "4:[f54<1.55949998] yes=9,no=10,missing=9,gain=2.49325705,cover=106.070824"
[962] "9:[f17<0.394999981] yes=17,no=18,missing=17,gain=2.88667846,cover=19.9598236"
[963] "17:[f0<0.075000003] yes=23,no=24,missing=23,gain=2.65784955,cover=16.5503426"
[964] "23:[f54<1.5395] yes=31,no=32,missing=31,gain=1.8514396,cover=14.3113585"
[965] "31:leaf=0.0419695824,cover=13.1760397"
[966] "32:leaf=0.347995967,cover=1.13531959"
[967] "24:leaf=-0.212544218,cover=2.23898458"
[968] "18:[f20<0.939999998] yes=25,no=26,missing=25,gain=0.683146954,cover=3.40947986"
[969] "25:leaf=0.0579550304,cover=1.173105"
[970] "26:leaf=0.387776703,cover=2.23637486"
[971] "10:[f54<1.9375] yes=19,no=20,missing=19,gain=4.7167511,cover=86.1110001"
[972] "19:[f7<0.295000017] yes=27,no=28,missing=27,gain=4.62120438,cover=15.0347061"
[973] "27:[f56<169.5] yes=33,no=34,missing=33,gain=2.19785929,cover=13.7201042"
[974] "33:leaf=-0.269210398,cover=11.4548407"
[975] "34:leaf=0.0372274406,cover=2.26526332"
[976] "28:leaf=0.250386387,cover=1.3146019"
[977] "20:[f17<0.234999999] yes=29,no=30,missing=29,gain=3.98820734,cover=71.0762939"
[978] "29:[f36<0.0649999976] yes=35,no=36,missing=35,gain=2.92805052,cover=58.2977142"
[979] "35:leaf=0.076606974,cover=49.5112991"
[980] "36:leaf=-0.103457466,cover=8.78641605"
[981] "30:[f1<0.00999999978] yes=37,no=38,missing=37,gain=6.08018494,cover=12.7785778"
[982] "37:leaf=-0.283387274,cover=7.78680038"
[983] "38:leaf=0.114229895,cover=4.99177694"
[984] "2:[f51<5.02699995] yes=5,no=6,missing=5,gain=0.756679535,cover=7.04230404"
[985] "5:[f56<14.5] yes=11,no=12,missing=11,gain=0.238707542,cover=5.8104949"
[986] "11:leaf=0.0643231422,cover=2.07158041"
[987] "12:leaf=0.233793244,cover=3.73891425"
[988] "6:leaf=-0.0378925577,cover=1.23180926"
[989] "booster[22]"
[990] "0:[f49<0.611999989] yes=1,no=2,missing=1,gain=2.34435296,cover=125.515762"
[991] "1:[f6<0.625] yes=3,no=4,missing=3,gain=2.73068452,cover=120.435921"
[992] "3:[f7<0.314999998] yes=7,no=8,missing=7,gain=2.21876407,cover=114.393677"
[993] "7:[f5<0.829999983] yes=11,no=12,missing=11,gain=2.42314434,cover=106.084534"
[994] "11:[f56<2.5] yes=17,no=18,missing=17,gain=2.04381824,cover=103.629105"
[995] "17:leaf=0.257982522,cover=1.27085638"
[996] "18:[f9<3.51499987] yes=23,no=24,missing=23,gain=2.13122559,cover=102.358246"
[997] "23:leaf=-0.0363500901,cover=101.059113"
[998] "24:leaf=0.256409049,cover=1.29913807"
[999] "12:leaf=0.231266692,cover=2.45542765"
[1000] "8:[f18<2.01999998] yes=13,no=14,missing=13,gain=1.58832896,cover=8.30914783"
[ reached getOption("max.print") -- omitted 478 entries ]xgb.plot.tree(model=xgb1)Define hyperparameter grid, really big! = really slow
ntrees<-42
hyper_grid<-expand.grid(
eta=2/ntrees,
max_depth=c(3,4,5,6,8,10),
min_child_weight=c(1,2,3),
subsample=c(0.5,0.75,1),
colsample_bytree=c(0.4,0.6,0.8,1),
gamma=c(0,1,3,10,100,1000),
error=0, # a place to dump error results
trees=0) # a place to dump required number of treesGrid search
for(i in seq_len(nrow(hyper_grid))) {
set.seed(123)
m<-xgb.cv(
data=train_data,
label=train_label,
nrounds=ntrees,
objective="binary:logistic",
early_stopping_rounds=10,
nfold=5,
verbose=1,
eval_metric="error",
params=list(
eta=hyper_grid$eta[i],
max_depth=hyper_grid$max_depth[i],
min_child_weight=hyper_grid$min_child_weight[i],
subsample=hyper_grid$subsample[i],
colsample_bytree=hyper_grid$colsample_bytree[i],
gamma=hyper_grid$gamma[i])
)
#hyper_grid$logloss[i]<-min(m$evaluation_log$test_logloss_mean)
hyper_grid$error[i]<-min(m$evaluation_log$test_error_mean)
hyper_grid$trees[i]<-m$best_iteration}Results of search
hyper_grid %>%
filter(error>0) %>%
arrange(error) %>%
glimpse()Optimal parameter list from results of grid search
params<-list(
eta=0.04761905,
max_depth=10,
min_child_weight=1,
subsample=0.75,
colsample_bytree=0.6,
gamma=0)Train final model
set.seed(123)
xgb_final<-xgboost(
params=params,
data=train_data,
label=train_label,
nrounds=31,
objective="binary:logistic",
early_stopping_rounds=10,
nfold=5,
verbose = 0,
eval_metric="error")[18:59:46] WARNING: amalgamation/../src/learner.cc:573:
Parameters: { "nfold" } might not be used.
This may not be accurate due to some parameters are only used in language bindings but
passed down to XGBoost core. Or some parameters are not used but slip through this
verification. Please open an issue if you find above cases.xgb_pred_final<-predict(xgb_final,test_data)
library(pROC)
my_roc_final<-roc(test_label,xgb_pred_final)Setting levels: control = 0, case = 1
Setting direction: controls < casescoords(my_roc_final,"best",ret=c("threshold","specificity","sensitivity","accuracy",
"precision","recall"),transpose=FALSE)Importance matrix
importance_matrix<-xgb.importance(model=xgb_final)
print(importance_matrix)xgb.plot.importance(importance_matrix=importance_matrix)
xgb.dump(xgb_final,with_stats=TRUE) [1] "booster[0]"
[2] "0:[f6<0.00999999978] yes=1,no=2,missing=1,gain=589.312073,cover=604.75"
[3] "1:[f15<0.13499999] yes=3,no=4,missing=3,gain=399.843567,cover=501.5"
[4] "3:[f54<3.3829999] yes=7,no=8,missing=7,gain=175.670593,cover=409.75"
[5] "7:[f23<0.00999999978] yes=13,no=14,missing=13,gain=56.6517944,cover=334.75"
[6] "13:[f22<0.425000012] yes=21,no=22,missing=21,gain=24.3152466,cover=322.25"
[7] "21:[f16<0.935000002] yes=33,no=34,missing=33,gain=9.45025635,cover=319"
[8] "33:[f21<0.135000005] yes=49,no=50,missing=49,gain=8.48516846,cover=314.25"
[9] "49:[f18<3.69500017] yes=65,no=66,missing=65,gain=6.29571533,cover=312.75"
[10] "65:[f10<0.555000007] yes=81,no=82,missing=81,gain=7.43847656,cover=276.25"
[11] "81:[f55<27.5] yes=97,no=98,missing=97,gain=2.51300049,cover=275.25"
[12] "97:leaf=-0.0888824686,cover=246.25"
[13] "98:leaf=-0.0666666701,cover=29"
[14] "82:leaf=0.0238095243,cover=1"
[15] "66:[f54<2.05350018] yes=83,no=84,missing=83,gain=12.4529457,cover=36.5"
[16] "83:[f17<1.75999999] yes=99,no=100,missing=99,gain=2.68929291,cover=27.25"
[17] "99:leaf=-0.0795107037,cover=26.25"
[18] "100:leaf=-0,cover=1"
[19] "84:[f2<0.119999997] yes=101,no=102,missing=101,gain=3.57943296,cover=9.25"
[20] "101:leaf=-0.0231660232,cover=8.25"
[21] "102:leaf=0.0476190485,cover=1"
[22] "50:leaf=0.0190476198,cover=1.5"
[23] "34:[f18<2.64499998] yes=51,no=52,missing=51,gain=5.94202852,cover=4.75"
[24] "51:[f2<0.0299999993] yes=67,no=68,missing=67,gain=1.01587319,cover=3.5"
[25] "67:leaf=-0.0544217713,cover=2.5"
[26] "68:leaf=-0,cover=1"
[27] "52:leaf=0.052910056,cover=1.25"
[28] "22:[f55<9.5] yes=35,no=36,missing=35,gain=3.84841633,cover=3.25"
[29] "35:leaf=-0.0238095243,cover=1"
[30] "36:leaf=0.0659340695,cover=2.25"
[31] "14:[f7<0.00999999978] yes=23,no=24,missing=23,gain=8.47058868,cover=12.5"
[32] "23:[f10<0.0399999991] yes=37,no=38,missing=37,gain=6.70084047,cover=7.5"
[33] "37:[f18<6.23499966] yes=53,no=54,missing=53,gain=6.59523869,cover=6"
[34] "53:[f16<0.170000002] yes=69,no=70,missing=69,gain=5.13333321,cover=5"
[35] "69:leaf=-0.0761904791,cover=4"
[36] "70:leaf=0.0238095243,cover=1"
[37] "54:leaf=0.0476190485,cover=1"
[38] "38:leaf=0.0571428612,cover=1.5"
[39] "24:[f11<0.639999986] yes=39,no=40,missing=39,gain=2.13333321,cover=5"
[40] "39:leaf=0.0761904791,cover=4"
[41] "40:leaf=-0,cover=1"
[42] "8:[f26<0.0199999996] yes=15,no=16,missing=15,gain=73.8250122,cover=75"
[43] "15:[f25<0.129999995] yes=25,no=26,missing=25,gain=35.0212708,cover=59.75"
[44] "25:[f23<0.0199999996] yes=41,no=42,missing=41,gain=19.5713539,cover=54.5"
[45] "41:[f22<0.075000003] yes=55,no=56,missing=55,gain=17.7731094,cover=41.5"
[46] "55:[f7<0.425000012] yes=71,no=72,missing=71,gain=9.29775715,cover=34"
[47] "71:[f11<0.100000001] yes=85,no=86,missing=85,gain=8.4114027,cover=30.75"
[48] "85:[f49<0.0914999992] yes=103,no=104,missing=103,gain=10.3703451,cover=20"
[49] "103:leaf=-0.0043956046,cover=15.25"
[50] "104:leaf=0.0703933761,cover=4.75"
[51] "86:[f18<2.0999999] yes=105,no=106,missing=105,gain=8.96217537,cover=10.75"
[52] "105:leaf=-0.0582010597,cover=8"
[53] "106:leaf=0.0317460336,cover=2.75"
[54] "72:leaf=0.0728291348,cover=3.25"
[55] "56:leaf=0.0840336159,cover=7.5"
[56] "42:leaf=0.0884353742,cover=13"
[57] "26:leaf=-0.0799999982,cover=5.25"
[58] "16:leaf=-0.0864468887,cover=15.25"
[59] "4:[f26<0.0250000004] yes=9,no=10,missing=9,gain=49.1412201,cover=91.75"
[60] "9:[f54<2.18300009] yes=17,no=18,missing=17,gain=18.7777252,cover=86.25"
[61] "17:[f25<0.115000002] yes=27,no=28,missing=27,gain=8.60012436,cover=25.25"
[62] "27:[f54<1.29850006] yes=43,no=44,missing=43,gain=8.24489784,cover=23.5"
[63] "43:[f18<2.09500003] yes=57,no=58,missing=57,gain=3.73333359,cover=3.5"
[64] "57:leaf=-0.0634920672,cover=2"
[65] "58:leaf=0.0190476198,cover=1.5"
[66] "44:[f55<36.5] yes=59,no=60,missing=59,gain=6.5714283,cover=20"
[67] "59:[f49<0.291999996] yes=73,no=74,missing=73,gain=2.8571434,cover=19"
[68] "73:[f2<1.47000003] yes=87,no=88,missing=87,gain=2.1428566,cover=16.5"
[69] "87:[f0<0.200000003] yes=107,no=108,missing=107,gain=3.5714283,cover=15"
[70] "107:leaf=0.0680272132,cover=13"
[71] "108:leaf=-0,cover=2"
[72] "88:leaf=-0,cover=1.5"
[73] "74:[f15<0.909999967] yes=89,no=90,missing=89,gain=2,cover=2.5"
[74] "89:leaf=0.0317460336,cover=1.25"
[75] "90:leaf=-0.0317460336,cover=1.25"
[76] "60:leaf=-0.0476190485,cover=1"
[77] "28:leaf=-0.0606060624,cover=1.75"
[78] "18:[f18<0.610000014] yes=29,no=30,missing=29,gain=5.77859497,cover=61"
[79] "29:[f54<2.7249999] yes=45,no=46,missing=45,gain=7.32323217,cover=7.25"
[80] "45:leaf=-0.052910056,cover=1.25"
[81] "46:[f9<0.144999996] yes=61,no=62,missing=61,gain=1.16666698,cover=6"
[82] "61:[f18<0.100000001] yes=75,no=76,missing=75,gain=1.63333321,cover=5"
[83] "75:leaf=0.0666666701,cover=4"
[84] "76:leaf=-0,cover=1"
[85] "62:leaf=-0,cover=1"
[86] "30:[f17<2.15999985] yes=47,no=48,missing=47,gain=6.37609863,cover=53.75"
[87] "47:[f49<0.127999991] yes=63,no=64,missing=63,gain=2.13282776,cover=52.75"
[88] "63:[f18<1.05999994] yes=77,no=78,missing=77,gain=0.800415039,cover=34.75"
[89] "77:[f2<0.400000006] yes=91,no=92,missing=91,gain=0.666666627,cover=2"
[90] "91:leaf=-0,cover=1"
[91] "92:leaf=0.0476190485,cover=1"
[92] "78:leaf=0.0895943567,cover=32.75"
[93] "64:[f17<0.375] yes=79,no=80,missing=79,gain=7.35269928,cover=18"
[94] "79:[f54<3.94199991] yes=93,no=94,missing=93,gain=1.06476593,cover=15.5"
[95] "93:[f18<2.38499999] yes=109,no=110,missing=109,gain=3.13725519,cover=7.5"
[96] "109:leaf=0.0634920672,cover=5"
[97] "110:leaf=-0,cover=2.5"
[98] "94:leaf=0.0846560895,cover=8"
[99] "80:[f15<0.965000033] yes=95,no=96,missing=95,gain=1.81428564,cover=2.5"
[100] "95:leaf=-0.0380952395,cover=1.5"
[101] "96:leaf=0.0238095243,cover=1"
[102] "48:leaf=-0.0238095243,cover=1"
[103] "10:leaf=-0.0805860832,cover=5.5"
[104] "2:[f26<0.0799999982] yes=5,no=6,missing=5,gain=25.7249451,cover=103.25"
[105] "5:[f25<0.104999997] yes=11,no=12,missing=11,gain=9.02896118,cover=101.25"
[106] "11:[f7<2.13000011] yes=19,no=20,missing=19,gain=3.4239502,cover=99.75"
[107] "19:[f9<2.00999999] yes=31,no=32,missing=31,gain=0.246490479,cover=98.75"
[108] "31:leaf=0.0889692605,cover=97.75"
[109] "32:leaf=0.0238095243,cover=1"
[110] "20:leaf=-0,cover=1"
[111] "12:leaf=-0.0190476198,cover=1.5"
[112] "6:leaf=-0.0634920672,cover=2"
[113] "booster[1]"
[114] "0:[f52<0.0304999985] yes=1,no=2,missing=1,gain=727.635864,cover=605.499878"
[115] "1:[f15<0.13499999] yes=3,no=4,missing=3,gain=251.304047,cover=440.523438"
[116] "3:[f23<0.00999999978] yes=7,no=8,missing=7,gain=74.352417,cover=374.117676"
[117] "7:[f54<6.9829998] yes=15,no=16,missing=15,gain=60.2094727,cover=360.639679"
[118] "15:[f4<0.629999995] yes=25,no=26,missing=25,gain=20.6796875,cover=350.653046"
[119] "25:[f22<0.140000001] yes=41,no=42,missing=41,gain=9.23016357,cover=315.463745"
[120] "41:[f5<1.04999995] yes=61,no=62,missing=61,gain=5.69628906,cover=311.968597"
[121] "61:[f8<0.805000007] yes=77,no=78,missing=77,gain=4.10559082,cover=307.974121"
[122] "77:[f54<3.16699982] yes=91,no=92,missing=91,gain=2.88726807,cover=304.230255"
[123] "91:[f56<2.5] yes=99,no=100,missing=99,gain=2.21350098,cover=256.78653"
[124] "99:leaf=-0.0243796818,cover=2.49556088"
[125] "100:leaf=-0.0828087479,cover=254.29097"
[126] "92:[f18<4.06500006] yes=101,no=102,missing=101,gain=10.0520859,cover=47.4437332"
[127] "101:leaf=-0.0686316118,cover=45.9443626"
[128] "102:leaf=0.0377926491,cover=1.49937344"
[129] "78:[f8<0.935000002] yes=93,no=94,missing=93,gain=4.85365105,cover=3.74385333"
[130] "93:leaf=0.0283082183,cover=1.7474103"
[131] "94:leaf=-0.0609099828,cover=1.99644303"
[132] "62:[f54<2.0625] yes=79,no=80,missing=79,gain=5.65626907,cover=3.99448943"
[133] "79:leaf=-0.0607873164,cover=1.99615359"
[134] "80:leaf=0.0325799994,cover=1.99833584"
[135] "42:[f36<0.0599999987] yes=63,no=64,missing=63,gain=4.45099974,cover=3.49513626"
[136] "63:leaf=0.0352765359,cover=2.24714494"
[137] "64:leaf=-0.0518909357,cover=1.2479912"
[138] "26:[f17<0.319999993] yes=43,no=44,missing=43,gain=22.7775154,cover=35.1892891"
[139] "43:[f54<2.71799994] yes=65,no=66,missing=65,gain=20.7531738,cover=28.7010078"
[140] "65:[f7<0.310000002] yes=81,no=82,missing=81,gain=4.97491837,cover=24.7062893"
[141] "81:leaf=-0.0761589333,cover=23.4581013"
[142] "82:leaf=0.0124989171,cover=1.24818897"
[143] "66:[f49<0.140500009] yes=83,no=84,missing=83,gain=5.59719801,cover=3.99471855"
[144] "83:leaf=0.0689528659,cover=2.746943"
[145] "84:leaf=-0.0304969605,cover=1.24777567"
[146] "44:[f4<1.125] yes=67,no=68,missing=67,gain=4.06234121,cover=6.48827934"
[147] "67:leaf=0.0655134022,cover=3.7431314"
[148] "68:[f4<2.55999994] yes=85,no=86,missing=85,gain=2.49669719,cover=2.74514771"
[149] "85:leaf=-0.0371352807,cover=1.49749029"
[150] "86:leaf=0.0321862288,cover=1.24765754"
[151] "16:[f50<0.0520000011] yes=27,no=28,missing=27,gain=8.95179367,cover=9.98664665"
[152] "27:[f4<0.129999995] yes=45,no=46,missing=45,gain=2.34873486,cover=8.73872375"
[153] "45:leaf=0.0128414389,cover=2.49861574"
[154] "46:leaf=0.0726751834,cover=6.24010849"
[155] "28:leaf=-0.0508207045,cover=1.24792242"
[156] "8:[f44<0.335000008] yes=17,no=18,missing=17,gain=13.2936954,cover=13.4779968"
[157] "17:[f56<44] yes=29,no=30,missing=29,gain=6.15799904,cover=11.2314281"
[158] "29:leaf=-0.00663369056,cover=2.24762106"
[159] "30:leaf=0.0778560713,cover=8.98380661"
[160] "18:leaf=-0.0494143516,cover=2.24656916"
[161] "4:[f36<0.155000001] yes=9,no=10,missing=9,gain=43.0367775,cover=66.4057617"
[162] "9:[f56<71.5] yes=19,no=20,missing=19,gain=13.8356934,cover=56.666584"
[163] "19:[f15<1.29500008] yes=31,no=32,missing=31,gain=9.84400654,cover=14.7314777"
[164] "31:[f54<2.57500005] yes=47,no=48,missing=47,gain=5.37615347,cover=5.49293804"
[165] "47:leaf=-0.0644412562,cover=3.7464242"
[166] "48:leaf=0.0232984982,cover=1.74651372"
[167] "32:[f56<15] yes=49,no=50,missing=49,gain=2.85492039,cover=9.2385397"
[168] "49:leaf=-0.0067963358,cover=2.24760056"
[169] "50:[f15<2] yes=69,no=70,missing=69,gain=0.984498024,cover=6.99093962"
[170] "69:leaf=0.0136733996,cover=1.99717259"
[171] "70:leaf=0.0614776462,cover=4.99376678"
[172] "20:[f44<0.595000029] yes=33,no=34,missing=33,gain=7.58283997,cover=41.9351044"
[173] "33:[f24<0.25999999] yes=51,no=52,missing=51,gain=4.51979828,cover=37.6907539"
[174] "51:[f17<1.54499996] yes=71,no=72,missing=71,gain=2.98640442,cover=36.442009"
[175] "71:[f54<1.25999999] yes=87,no=88,missing=87,gain=1.70517731,cover=34.6936989"
[176] "87:leaf=0.0110452119,cover=1.24927473"
[177] "88:[f44<0.36500001] yes=95,no=96,missing=95,gain=1.16478729,cover=33.4444275"
[178] "95:leaf=0.0823824257,cover=29.199913"
[179] "96:[f15<0.49000001] yes=103,no=104,missing=103,gain=3.26275611,cover=4.24451208"
[180] "103:leaf=-0.00956203602,cover=1.74863219"
[181] "104:leaf=0.0654103681,cover=2.49587965"
[182] "72:leaf=0.00757208699,cover=1.7483077"
[183] "52:leaf=-0.0111143133,cover=1.2487458"
[184] "34:[f15<1.17499995] yes=53,no=54,missing=53,gain=6.86125135,cover=4.24435186"
[185] "53:[f15<0.659999967] yes=73,no=74,missing=73,gain=0.27674365,cover=2.49728346"
[186] "73:leaf=-0.0116651906,cover=1.24867892"
[187] "74:leaf=-0.0539749078,cover=1.24860454"
[188] "54:leaf=0.0583721586,cover=1.74706841"
[189] "10:[f54<4.83949995] yes=21,no=22,missing=21,gain=7.56612015,cover=9.73917866"
[190] "21:[f5<0.0700000003] yes=35,no=36,missing=35,gain=1.78663445,cover=7.99196053"
[191] "35:leaf=-0.0814862922,cover=6.74361467"
[192] "36:leaf=-0.0103558563,cover=1.24834561"
[193] "22:leaf=0.0244602449,cover=1.74721766"
[194] "2:[f24<0.38499999] yes=5,no=6,missing=5,gain=144.236908,cover=164.97644"
[195] "5:[f25<0.274999976] yes=11,no=12,missing=11,gain=15.4416504,cover=149.999008"
[196] "11:[f54<1.96799994] yes=23,no=24,missing=23,gain=11.4613953,cover=148.251205"
[197] "23:[f44<0.36500001] yes=37,no=38,missing=37,gain=10.3056374,cover=18.7206745"
[198] "37:[f49<0.315500021] yes=55,no=56,missing=55,gain=6.94949722,cover=15.4751301"
[199] "55:[f4<0.295000017] yes=75,no=76,missing=75,gain=0.891345978,cover=14.2274981"
[200] "75:[f7<0.404999971] yes=89,no=90,missing=89,gain=1.16283083,cover=6.24053574"
[201] "89:[f56<164.5] yes=97,no=98,missing=97,gain=1.38736224,cover=3.9937582"
[202] "97:leaf=-0.00650865352,cover=2.24667501"
[203] "98:leaf=0.0415354334,cover=1.74708331"
[204] "90:leaf=0.0646835938,cover=2.24677753"
[205] "76:leaf=0.0817231089,cover=7.9869628"
[206] "56:leaf=-0.030418098,cover=1.24763191"
[207] "38:[f18<1.81500006] yes=57,no=58,missing=57,gain=1.495543,cover=3.24554396"
[208] "57:leaf=-0.0461844578,cover=1.99625826"
[209] "58:leaf=0.0096332049,cover=1.2492857"
[210] "24:[f47<0.0350000001] yes=39,no=40,missing=39,gain=3.54589844,cover=129.530533"
[211] "39:[f49<0.652500033] yes=59,no=60,missing=59,gain=2.62768555,cover=127.533974"
[212] "59:leaf=0.0885355696,cover=126.285309"
[213] "60:leaf=0.0100343833,cover=1.248667"
[214] "40:leaf=0.0150343087,cover=1.9965539"
[215] "12:leaf=-0.0421506017,cover=1.74781096"
[216] "6:[f17<0.0199999996] yes=13,no=14,missing=13,gain=9.02999496,cover=14.9774208"
[217] "13:leaf=-0.0823541805,cover=13.7289476"
[218] "14:leaf=0.0314980745,cover=1.24847341"
[219] "booster[2]"
[220] "0:[f51<0.0785000026] yes=1,no=2,missing=1,gain=689.399597,cover=604.922607"
[221] "1:[f52<0.0855000019] yes=3,no=4,missing=3,gain=136.562744,cover=350.921936"
[222] "3:[f6<0.0700000003] yes=7,no=8,missing=7,gain=71.9536743,cover=322.081604"
[223] "7:[f15<0.114999995] yes=15,no=16,missing=15,gain=18.8831177,cover=308.637909"
[224] "15:[f21<0.135000005] yes=29,no=30,missing=29,gain=7.37658691,cover=283.963074"
[225] "29:[f22<0.350000024] yes=49,no=50,missing=49,gain=5.79736328,cover=280.715363"
[226] "49:[f5<1.755] yes=69,no=70,missing=69,gain=4.44927979,cover=277.975586"
[227] "69:[f11<5.97000027] yes=83,no=84,missing=83,gain=2.40509033,cover=276.479645"
[228] "83:[f13<1.79499996] yes=95,no=96,missing=95,gain=2.16815186,cover=275.235687"
[229] "95:[f55<139.5] yes=109,no=110,missing=109,gain=1.08966064,cover=272.747864"
[230] "109:leaf=-0.0803790614,cover=269.762482"
[231] "110:leaf=-0.0329243764,cover=2.98538971"
[232] "96:[f49<0.0890000015] yes=111,no=112,missing=111,gain=1.80898643,cover=2.48781562"
[233] "111:leaf=-0.0494958572,cover=1.24390841"
[234] "112:leaf=0.0139025794,cover=1.24390721"
[235] "84:leaf=-0.00701315794,cover=1.24396944"
[236] "70:leaf=0.00186895125,cover=1.4959234"
[237] "50:[f5<0.075000003] yes=71,no=72,missing=71,gain=5.71677303,cover=2.73978949"
[238] "71:leaf=-0.0548912585,cover=1.49535477"
[239] "72:leaf=0.0499893874,cover=1.24443483"
[240] "30:[f1<0.00999999978] yes=51,no=52,missing=51,gain=4.11364985,cover=3.24769354"
[241] "51:leaf=0.0329021998,cover=1.99830246"
[242] "52:leaf=-0.0523667186,cover=1.24939108"
[243] "16:[f4<1.08999991] yes=31,no=32,missing=31,gain=15.1575537,cover=24.6748314"
[244] "31:[f44<0.129999995] yes=53,no=54,missing=53,gain=3.78162384,cover=20.691061"
[245] "53:[f24<0.0649999976] yes=73,no=74,missing=73,gain=5.8757,cover=13.7050257"
[246] "73:[f15<0.49000001] yes=85,no=86,missing=85,gain=5.80108833,cover=9.71903801"
[247] "85:[f2<0.274999976] yes=97,no=98,missing=97,gain=1.15533161,cover=3.7337172"
[248] "97:leaf=0.0497398004,cover=2.23850894"
[249] "98:leaf=-0.000522373826,cover=1.49520826"
[250] "86:[f15<3.8499999] yes=99,no=100,missing=99,gain=1.78785872,cover=5.98532104"
[251] "99:[f55<8.5] yes=113,no=114,missing=113,gain=0.574775219,cover=4.73815346"
[252] "113:leaf=-0.0152662909,cover=1.99660349"
[253] "114:leaf=-0.0585467331,cover=2.74154973"
[254] "100:leaf=0.0100804362,cover=1.24716771"
[255] "74:leaf=-0.0722156316,cover=3.98598766"
[256] "54:leaf=-0.0757794008,cover=6.98603582"
[257] "32:[f20<0.774999976] yes=55,no=56,missing=55,gain=2.95007229,cover=3.98377085"
[258] "55:leaf=-0.0107377889,cover=1.24480593"
[259] "56:leaf=0.0665763095,cover=2.7389648"
[260] "8:[f24<0.150000006] yes=17,no=18,missing=17,gain=11.8051329,cover=13.443716"
[261] "17:[f55<9.5] yes=33,no=34,missing=33,gain=3.41461372,cover=11.2014666"
[262] "33:leaf=-0.000585695147,cover=1.99525082"
[263] "34:leaf=0.0672354549,cover=9.20621586"
[264] "18:leaf=-0.0484821051,cover=2.24224949"
[265] "4:[f24<0.239999995] yes=9,no=10,missing=9,gain=29.6108952,cover=28.8403187"
[266] "9:[f55<9.5] yes=19,no=20,missing=19,gain=10.0495682,cover=24.8646584"
[267] "19:[f52<0.203999996] yes=35,no=36,missing=35,gain=1.67757225,cover=2.49390268"
[268] "35:leaf=-0.0504332036,cover=1.24688053"
[269] "36:leaf=0.0113905491,cover=1.24702227"
[270] "20:[f49<0.291999996] yes=37,no=38,missing=37,gain=6.89044571,cover=22.3707561"
[271] "37:leaf=0.0742233396,cover=20.8751278"
[272] "38:leaf=-0.0201958939,cover=1.49562752"
[273] "10:leaf=-0.070726864,cover=3.97566009"
[274] "2:[f55<18.5] yes=5,no=6,missing=5,gain=184.583069,cover=254.000702"
[275] "5:[f52<0.0120000001] yes=11,no=12,missing=11,gain=69.6656265,cover=96.9849701"
[276] "11:[f6<0.25999999] yes=21,no=22,missing=21,gain=48.2731628,cover=76.3371887"
[277] "21:[f15<1.27999997] yes=39,no=40,missing=39,gain=33.9984856,cover=67.8696671"
[278] "39:[f51<0.779000044] yes=57,no=58,missing=57,gain=14.7715607,cover=62.3901138"
[279] "57:[f4<0.355000019] yes=75,no=76,missing=75,gain=10.3661728,cover=48.9578705"
[280] "75:[f18<5.90499973] yes=87,no=88,missing=87,gain=5.23381805,cover=39.746254"
[281] "87:[f10<0.125] yes=101,no=102,missing=101,gain=2.26559448,cover=38.4998474"
[282] "101:leaf=-0.0811866149,cover=37.2546349"
[283] "102:leaf=-0.00838435348,cover=1.24521542"
[284] "88:leaf=0.0116640758,cover=1.24640608"
[285] "76:[f44<0.25] yes=89,no=90,missing=89,gain=7.89983702,cover=9.21161461"
[286] "89:[f2<0.0599999987] yes=103,no=104,missing=103,gain=5.7839756,cover=5.97940636"
[287] "103:[f49<0.101499997] yes=115,no=116,missing=115,gain=2.16881323,cover=3.23477054"
[288] "115:leaf=0.0103868796,cover=1.74136364"
[289] "116:leaf=-0.0540913641,cover=1.49340689"
[290] "104:leaf=0.0567364432,cover=2.74463582"
[291] "90:leaf=-0.0679204091,cover=3.23220778"
[292] "58:[f44<0.784999967] yes=77,no=78,missing=77,gain=5.03041506,cover=13.4322433"
[293] "77:[f18<0.620000005] yes=91,no=92,missing=91,gain=4.38835478,cover=10.4507713"
[294] "91:[f51<1.52699995] yes=105,no=106,missing=105,gain=1.79689372,cover=4.22432804"
[295] "105:[f51<0.953500032] yes=117,no=118,missing=117,gain=0.648094654,cover=2.98142815"
[296] "117:leaf=-0.00718426798,cover=1.24412298"
[297] "118:leaf=-0.055701755,cover=1.73730528"
[298] "106:leaf=0.0145437503,cover=1.24289989"
[299] "92:[f51<0.861000001] yes=107,no=108,missing=107,gain=2.69151974,cover=6.22644281"
[300] "107:leaf=-0.0165394321,cover=1.49293303"
[301] "108:[f51<3.39050007] yes=119,no=120,missing=119,gain=0.243254662,cover=4.73351002"
[302] "119:leaf=0.0532473959,cover=3.48880768"
[303] "120:leaf=0.0130606499,cover=1.24470222"
[304] "78:leaf=-0.0543717146,cover=2.98147249"
[305] "40:[f51<0.236499995] yes=59,no=60,missing=59,gain=1.30806923,cover=5.47955084"
[306] "59:leaf=0.00950985588,cover=1.24590182"
[307] "60:leaf=0.0729053468,cover=4.23364925"
[308] "22:[f18<0.720000029] yes=41,no=42,missing=41,gain=1.63842773,cover=8.46752548"
[309] "41:leaf=0.0107238395,cover=1.24800253"
[310] "42:leaf=0.0794843361,cover=7.21952248"
[311] "12:[f55<6.5] yes=23,no=24,missing=23,gain=4.41383743,cover=20.6477814"
[312] "23:leaf=-0.000658857403,cover=1.98919165"
[313] "24:[f18<0.290000021] yes=43,no=44,missing=43,gain=0.77425766,cover=18.6585903"
[314] "43:leaf=0.0232211072,cover=1.74281502"
[315] "44:[f8<0.420000017] yes=61,no=62,missing=61,gain=0.506996155,cover=16.9157753"
[316] "61:leaf=0.0811675787,cover=14.9228935"
[317] "62:leaf=0.0293264706,cover=1.99288142"
[318] "6:[f24<0.409999996] yes=13,no=14,missing=13,gain=35.1903992,cover=157.015732"
[319] "13:[f20<0.414999992] yes=25,no=26,missing=25,gain=8.53338623,cover=152.532776"
[320] "25:[f51<0.379999995] yes=45,no=46,missing=45,gain=8.49247742,cover=22.3985634"
[321] "45:[f52<0.00850000046] yes=63,no=64,missing=63,gain=8.2210741,cover=9.20887375"
[322] "63:[f15<0.284999996] yes=79,no=80,missing=79,gain=6.72389746,cover=5.72662163"
[323] "79:[f11<0.189999998] yes=93,no=94,missing=93,gain=0.890336514,cover=3.98413205"
[324] "93:leaf=-0.00987444166,cover=1.24404383"
[325] "94:leaf=-0.0662995651,cover=2.74008822"
[326] "80:leaf=0.040128503,cover=1.7424897"
[327] "64:leaf=0.0596769713,cover=3.48225188"
[328] "46:[f20<0.280000001] yes=65,no=66,missing=65,gain=0.774101257,cover=13.1896896"
[329] "65:leaf=0.0755630955,cover=11.4495935"
[330] "66:leaf=0.0221464802,cover=1.74009573"
[331] "26:[f24<0.155000001] yes=47,no=48,missing=47,gain=2.48550415,cover=130.134216"
[332] "47:[f18<0.0799999982] yes=67,no=68,missing=67,gain=0.468231201,cover=127.894424"
[333] "67:[f49<0.265500009] yes=81,no=82,missing=81,gain=2.73564482,cover=3.97826147"
[334] "81:leaf=0.0646365583,cover=2.73173094"
[335] "82:leaf=-0.00999665819,cover=1.24653053"
[336] "68:leaf=0.0850202292,cover=123.916168"
[337] "48:leaf=0.0211956482,cover=2.23978972"
[338] "14:[f52<0.0305000003] yes=27,no=28,missing=27,gain=0.715229511,cover=4.4829545"
[339] "27:leaf=-0.0583592281,cover=3.2391057"
[340] "28:leaf=-0.00865621958,cover=1.24384904"
[341] "booster[3]"
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[343] "1:[f6<0.0199999996] yes=3,no=4,missing=3,gain=150.420532,cover=341.597198"
[344] "3:[f23<0.0350000001] yes=7,no=8,missing=7,gain=50.0408936,cover=317.835327"
[345] "7:[f15<0.170000002] yes=15,no=16,missing=15,gain=20.8162231,cover=303.961304"
[346] "15:[f21<0.135000005] yes=29,no=30,missing=29,gain=5.57080078,cover=283.575409"
[347] "29:[f13<1.79499996] yes=47,no=48,missing=47,gain=2.51080322,cover=279.831512"
[348] "47:[f17<3.32999992] yes=67,no=68,missing=67,gain=2.23919678,cover=276.602051"
[349] "67:[f54<4.48799992] yes=89,no=90,missing=89,gain=1.18359375,cover=275.363831"
[350] "89:leaf=-0.0777789503,cover=262.474182"
[351] "90:[f1<0.159999996] yes=101,no=102,missing=101,gain=1.06939697,cover=12.8896618"
[352] "101:[f54<4.78849983] yes=109,no=110,missing=109,gain=1.43394279,cover=10.9085579"
[353] "109:leaf=-0.0130249038,cover=1.98558581"
[354] "110:leaf=-0.0656220615,cover=8.92297268"
[355] "102:leaf=-0.012254511,cover=1.98110378"
[356] "68:leaf=-0.00668866094,cover=1.23820841"
[357] "48:[f13<2.61999989] yes=69,no=70,missing=69,gain=4.51277637,cover=3.22946453"
[358] "69:leaf=0.0327259302,cover=1.24301982"
[359] "70:leaf=-0.0586781465,cover=1.98644471"
[360] "30:[f51<0.0445000008] yes=49,no=50,missing=49,gain=5.10297823,cover=3.74390078"
[361] "49:leaf=0.0321601406,cover=1.99820018"
[362] "50:leaf=-0.0582681,cover=1.74570072"
[363] "16:[f49<0.0844999999] yes=31,no=32,missing=31,gain=5.86423635,cover=20.3858871"
[364] "31:[f49<0.0194999985] yes=51,no=52,missing=51,gain=11.7069187,cover=11.932538"
[365] "51:[f54<1.80599999] yes=71,no=72,missing=71,gain=2.31306505,cover=8.21798706"
[366] "71:[f15<2.06500006] yes=91,no=92,missing=91,gain=2.29597425,cover=4.73423815"
[367] "91:leaf=-0.0691730231,cover=3.23864269"
[368] "92:leaf=-0.000327789428,cover=1.49559522"
[369] "72:[f55<27] yes=93,no=94,missing=93,gain=5.76076651,cover=3.48374915"
[370] "93:leaf=0.043294955,cover=1.98826265"
[371] "94:leaf=-0.0547234267,cover=1.49548662"
[372] "52:leaf=0.0595811047,cover=3.7145505"
[373] "32:[f2<0.50999999] yes=53,no=54,missing=53,gain=2.74225998,cover=8.45334911"
[374] "53:[f15<0.810000002] yes=73,no=74,missing=73,gain=1.26352501,cover=7.20866346"
[375] "73:leaf=-0.0745116994,cover=4.9711833"
[376] "74:leaf=-0.0197672285,cover=2.23748016"
[377] "54:leaf=0.00807779934,cover=1.24468565"
[378] "8:[f49<0.0729999989] yes=17,no=18,missing=17,gain=16.4609451,cover=13.8740311"
[379] "17:[f50<0.00499999989] yes=33,no=34,missing=33,gain=1.03167152,cover=8.90846634"
[380] "33:[f24<0.0250000004] yes=55,no=56,missing=55,gain=1.33690834,cover=7.66970158"
[381] "55:leaf=0.0697023124,cover=6.43332624"
[382] "56:leaf=0.0083600143,cover=1.23637533"
[383] "34:leaf=0.00793683995,cover=1.238765"
[384] "18:[f54<4.15450001] yes=35,no=36,missing=35,gain=2.55525351,cover=4.96556425"
[385] "35:leaf=-0.0608112551,cover=3.72483969"
[386] "36:leaf=0.00943190884,cover=1.2407248"
[387] "4:[f26<0.109999999] yes=9,no=10,missing=9,gain=17.6271477,cover=23.7618694"
[388] "9:[f24<0.274999976] yes=19,no=20,missing=19,gain=2.75849915,cover=21.7731228"
[389] "19:[f55<9.5] yes=37,no=38,missing=37,gain=2.43003082,cover=20.2776852"
[390] "37:leaf=0.0135879666,cover=1.99381018"
[391] "38:leaf=0.076996848,cover=18.2838745"
[392] "20:leaf=0.00282492186,cover=1.4954375"
[393] "10:leaf=-0.0600437,cover=1.9887476"
[394] "2:[f55<17.5] yes=5,no=6,missing=5,gain=167.022537,cover=247.151566"
[395] "5:[f15<0.295000017] yes=11,no=12,missing=11,gain=52.0200615,cover=89.5504227"
[396] "11:[f6<0.0700000003] yes=21,no=22,missing=21,gain=31.2267094,cover=67.7447662"
[397] "21:[f54<3.296] yes=39,no=40,missing=39,gain=24.4002113,cover=61.5479622"
[398] "39:[f16<0.329999983] yes=57,no=58,missing=57,gain=16.1378403,cover=58.3181343"
[399] "57:[f51<1.28299999] yes=75,no=76,missing=75,gain=7.0642395,cover=52.8561134"
[400] "75:[f17<0.379999995] yes=95,no=96,missing=95,gain=4.23246002,cover=48.6305885"
[401] "95:[f51<0.477999985] yes=103,no=104,missing=103,gain=0.867202759,cover=40.7316246"
[402] "103:leaf=-0.0764045492,cover=28.1217175"
[403] "104:[f55<7.5] yes=111,no=112,missing=111,gain=2.92728043,cover=12.60991"
[404] "111:leaf=-0.0689148456,cover=6.91220903"
[405] "112:leaf=-0.0193070155,cover=5.6977005"
[406] "96:[f17<0.720000029] yes=105,no=106,missing=105,gain=12.1116123,cover=7.89896345"
[407] "105:[f2<0.349999994] yes=113,no=114,missing=113,gain=1.98879123,cover=3.22197628"
[408] "113:leaf=-0.00743349455,cover=1.23644364"
[409] "114:leaf=0.0591642857,cover=1.98553252"
[410] "106:leaf=-0.0698255375,cover=4.67698717"
[411] "76:[f54<1.32550001] yes=97,no=98,missing=97,gain=1.86812901,cover=4.22552443"
[412] "97:leaf=-0.0265362076,cover=1.98472643"
[413] "98:leaf=0.0256816912,cover=2.240798"
[414] "58:[f24<0.324999988] yes=77,no=78,missing=77,gain=10.6260166,cover=5.46202087"
[415] "77:leaf=0.0645060092,cover=3.73238349"
[416] "78:leaf=-0.0547006242,cover=1.72963727"
[417] "40:leaf=0.0701248124,cover=3.2298274"
[418] "22:leaf=0.0626681969,cover=6.19680738"
[419] "12:[f51<0.119000003] yes=23,no=24,missing=23,gain=6.85263824,cover=21.8056564"
[420] "23:leaf=-0.0208840184,cover=2.23040128"
[421] "24:[f55<4.5] yes=41,no=42,missing=41,gain=3.42212677,cover=19.5752544"
[422] "41:leaf=1.27744479e-05,cover=1.98830438"
[423] "42:[f49<0.33950001] yes=59,no=60,missing=59,gain=0.881706238,cover=17.5869503"
[424] "59:[f12<0.0949999988] yes=79,no=80,missing=79,gain=0.584262848,cover=15.111351"
[425] "79:leaf=0.0746534765,cover=13.3700171"
[426] "80:leaf=0.0235321466,cover=1.74133444"
[427] "60:[f51<0.324000001] yes=81,no=82,missing=81,gain=0.0198047757,cover=2.47559857"
[428] "81:leaf=0.0295867808,cover=1.2371887"
[429] "82:leaf=0.00830188207,cover=1.23840988"
[430] "6:[f24<0.344999999] yes=13,no=14,missing=13,gain=33.0574036,cover=157.601151"
[431] "13:[f54<2.93350005] yes=25,no=26,missing=25,gain=8.57696533,cover=152.136871"
[432] "25:[f51<0.291999996] yes=43,no=44,missing=43,gain=7.75037766,cover=30.3923931"
[433] "43:[f16<0.00999999978] yes=61,no=62,missing=61,gain=7.40074921,cover=13.3594875"
[434] "61:[f23<0.0799999982] yes=83,no=84,missing=83,gain=5.90495396,cover=9.65719604"
[435] "83:[f6<0.224999994] yes=99,no=100,missing=99,gain=6.16946507,cover=7.18802786"
[436] "99:[f55<21] yes=107,no=108,missing=107,gain=2.7890029,cover=5.95612288"
[437] "107:leaf=0.0109646153,cover=1.24035704"
[438] "108:leaf=-0.0603096113,cover=4.71576595"
[439] "100:leaf=0.0469491072,cover=1.23190463"
[440] "84:leaf=0.0478798598,cover=2.46916795"
[441] "62:leaf=0.0677964538,cover=3.70229197"
[442] "44:[f54<1.79400003] yes=63,no=64,missing=63,gain=1.89260101,cover=17.0329056"
[443] "63:leaf=0.00625334959,cover=1.23964179"
[444] "64:[f54<2.88400006] yes=85,no=86,missing=85,gain=1.86385727,cover=15.7932634"
[445] "85:leaf=0.0773158371,cover=14.5552549"
[446] "86:leaf=0.00865286496,cover=1.23800862"
[447] "26:[f48<0.778999984] yes=45,no=46,missing=45,gain=5.32922363,cover=121.744476"
[448] "45:[f41<0.119999997] yes=65,no=66,missing=65,gain=0.15713501,cover=120.508026"
[449] "65:[f11<2.61500001] yes=87,no=88,missing=87,gain=0.00341796875,cover=119.278679"
[450] "87:leaf=0.0811705813,cover=118.045425"
[451] "88:leaf=0.0267668199,cover=1.23325491"
[452] "66:leaf=0.0251773465,cover=1.22934616"
[453] "46:leaf=-0.0109286662,cover=1.23645461"
[454] "14:[f15<0.74000001] yes=27,no=28,missing=27,gain=9.65584564,cover=5.46427441"
[455] "27:leaf=-0.0711608082,cover=4.21730804"
[456] "28:leaf=0.0505645685,cover=1.2469666"
[457] "booster[4]"
[458] "0:[f52<0.0555000007] yes=1,no=2,missing=1,gain=596.560303,cover=585.826843"
[459] "1:[f6<0.0649999976] yes=3,no=4,missing=3,gain=210.935638,cover=440.175964"
[460] "3:[f15<0.295000017] yes=7,no=8,missing=7,gain=122.710144,cover=401.657013"
[461] "7:[f23<0.00999999978] yes=15,no=16,missing=15,gain=45.4223633,cover=358.229828"
[462] "15:[f10<0.219999999] yes=23,no=24,missing=23,gain=13.492981,cover=343.943695"
[463] "23:[f24<0.0299999993] yes=33,no=34,missing=33,gain=9.61889648,cover=335.086975"
[464] "33:[f55<12.5] yes=47,no=48,missing=47,gain=24.495575,cover=220.152237"
[465] "47:[f16<0.135000005] yes=59,no=60,missing=59,gain=5.65133667,cover=159.113602"
[466] "59:[f22<0.549999952] yes=73,no=74,missing=73,gain=1.84967041,cover=155.41478"
[467] "73:leaf=-0.0741220862,cover=154.16983"
[468] "74:leaf=-0.00831124838,cover=1.24493897"
[469] "60:[f18<1.29999995] yes=75,no=76,missing=75,gain=2.64821172,cover=3.69882703"
[470] "75:leaf=-0.0404176563,cover=1.95860636"
[471] "76:leaf=0.0250731986,cover=1.74022067"
[472] "48:[f45<0.0450000018] yes=61,no=62,missing=61,gain=11.9162865,cover=61.0386314"
[473] "61:[f26<0.0850000009] yes=77,no=78,missing=77,gain=15.3074656,cover=43.1256943"
[474] "77:[f1<0.0749999955] yes=85,no=86,missing=85,gain=9.96469975,cover=33.5707092"
[475] "85:leaf=0.00566601427,cover=28.8908978"
[476] "86:leaf=-0.0631838739,cover=4.67980909"
[477] "78:leaf=-0.0714203566,cover=9.55498695"
[478] "62:[f49<0.297500014] yes=79,no=80,missing=79,gain=8.13191223,cover=17.9129353"
[479] "79:leaf=-0.0769579411,cover=16.4344673"
[480] "80:leaf=0.0255006868,cover=1.47846735"
[481] "34:leaf=-0.0810545236,cover=114.934753"
[482] "24:[f1<0.394999981] yes=35,no=36,missing=35,gain=5.00277901,cover=8.85671329"
[483] "35:[f52<0.00949999969] yes=49,no=50,missing=49,gain=6.38903046,cover=7.61622524"
[484] "49:[f10<0.949999988] yes=63,no=64,missing=63,gain=1.36453247,cover=5.89572048"
[485] "63:leaf=-0.0588362738,cover=4.16939354"
[486] "64:leaf=-0.00600962294,cover=1.72632694"
[487] "50:leaf=0.0412374698,cover=1.72050488"
[488] "36:leaf=0.0559201092,cover=1.24048793"
[489] "16:[f45<0.0799999982] yes=25,no=26,missing=25,gain=10.6343689,cover=14.2861328"
[490] "25:[f24<0.49000001] yes=37,no=38,missing=37,gain=13.5664778,cover=12.0559101"
[491] "37:[f55<9] yes=51,no=52,missing=51,gain=6.63818169,cover=10.0777941"
[492] "51:leaf=-0.0277756229,cover=1.23945427"
[493] "52:leaf=0.0720474273,cover=8.83833981"
[494] "38:leaf=-0.0583580062,cover=1.97811615"
[495] "26:leaf=-0.0608948879,cover=2.23022318"
[496] "8:[f26<0.38499999] yes=17,no=18,missing=17,gain=22.3846645,cover=43.4271927"
[497] "17:[f45<0.100000001] yes=27,no=28,missing=27,gain=13.365099,cover=38.9767036"
[498] "27:[f55<10] yes=39,no=40,missing=39,gain=9.25891113,cover=34.7487526"
[499] "39:[f15<1.03499997] yes=53,no=54,missing=53,gain=4.71701431,cover=6.96017027"
[500] "53:[f56<71.5] yes=65,no=66,missing=65,gain=2.12921429,cover=3.47748947"
[501] "65:leaf=-0.0608593971,cover=2.23341298"
[502] "66:leaf=0.00730456412,cover=1.24407661"
[503] "54:[f55<4.5] yes=67,no=68,missing=67,gain=0.32680285,cover=3.48268056"
[504] "67:leaf=0.00755546661,cover=1.74579895"
[505] "68:leaf=0.0398023203,cover=1.73688161"
[506] "40:[f24<0.25] yes=55,no=56,missing=55,gain=8.27316284,cover=27.7885818"
[507] "55:[f17<0.194999993] yes=69,no=70,missing=69,gain=2.80357361,cover=25.8043804"
[508] "69:[f56<42] yes=81,no=82,missing=81,gain=1.570755,cover=18.4338932"
[509] "81:[f56<32.5] yes=87,no=88,missing=87,gain=1.58893752,cover=2.47279763"
[510] "87:leaf=0.0478643477,cover=1.23647594"
[511] "88:leaf=-0.012063059,cover=1.23632169"
[512] "82:leaf=0.0770608261,cover=15.9610949"
[513] "70:[f49<0.141499996] yes=83,no=84,missing=83,gain=8.04934216,cover=7.37048721"
[514] "83:[f18<3.30999994] yes=89,no=90,missing=89,gain=3.93698597,cover=6.12286663"
[515] "89:leaf=0.0688159987,cover=4.88601017"
[516] "90:leaf=-0.0147530353,cover=1.23685658"
[517] "84:leaf=-0.0533950105,cover=1.24762034"
[518] "56:leaf=-0.0284871068,cover=1.98420131"
[519] "28:[f15<0.549999952] yes=41,no=42,missing=41,gain=2.33258581,cover=4.22795343"
[520] "41:leaf=0.00944261532,cover=1.24128246"
[521] "42:leaf=-0.0588317141,cover=2.98667073"
[522] "18:leaf=-0.0710096508,cover=4.45048952"
[523] "4:[f26<0.140000001] yes=9,no=10,missing=9,gain=17.6122513,cover=38.5189285"
[524] "9:[f52<0.0464999974] yes=19,no=20,missing=19,gain=5.24456024,cover=36.5425415"
[525] "19:[f32<0.210000008] yes=29,no=30,missing=29,gain=2.1934433,cover=35.318203"
[526] "29:[f24<0.0450000018] yes=43,no=44,missing=43,gain=1.69458008,cover=34.0860405"
[527] "43:leaf=0.0759857446,cover=32.8386307"
[528] "44:leaf=0.0100697447,cover=1.24740934"
[529] "30:leaf=0.00496745529,cover=1.23216355"
[530] "20:leaf=-0.0168595798,cover=1.22433853"
[531] "10:leaf=-0.0572420359,cover=1.97638667"
[532] "2:[f24<0.399999976] yes=5,no=6,missing=5,gain=49.9311218,cover=145.650864"
[533] "5:[f45<0.639999986] yes=11,no=12,missing=11,gain=15.6723328,cover=139.025162"
[534] "11:[f55<4.5] yes=21,no=22,missing=21,gain=12.3943481,cover=137.53334"
[535] "21:leaf=-0.047892753,cover=1.23725557"
[536] "22:[f26<0.210000008] yes=31,no=32,missing=31,gain=8.43725586,cover=136.296082"
[537] "31:[f49<0.652500033] yes=45,no=46,missing=45,gain=3.39312744,cover=135.066406"
[538] "45:[f24<0.199999988] yes=57,no=58,missing=57,gain=1.10760498,cover=133.323914"
[539] "57:leaf=0.0784110725,cover=130.356247"
[540] "58:[f2<1.11500001] yes=71,no=72,missing=71,gain=2.10048294,cover=2.96767735"
[541] "71:leaf=-0.0118471812,cover=1.23480511"
[542] "72:leaf=0.0553621762,cover=1.73287225"
[543] "46:leaf=0.00662600575,cover=1.74249053"
[544] "32:leaf=-0.0295839366,cover=1.22966909"
[545] "12:leaf=-0.0548749156,cover=1.4918263"
[546] "6:[f9<0.115000002] yes=13,no=14,missing=13,gain=2.28522587,cover=6.62570667"
[547] "13:leaf=-0.0693110675,cover=4.89466047"
[548] "14:leaf=-0.0043734163,cover=1.73104632"
[549] "booster[5]"
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[551] "1:[f6<0.0299999993] yes=3,no=4,missing=3,gain=219.928741,cover=447.051483"
[552] "3:[f15<0.295000017] yes=7,no=8,missing=7,gain=93.7509766,cover=406.289612"
[553] "7:[f23<0.00999999978] yes=15,no=16,missing=15,gain=43.5281982,cover=362.37677"
[554] "15:[f24<0.0299999993] yes=23,no=24,missing=23,gain=11.8459473,cover=349.90332"
[555] "23:[f55<12.5] yes=33,no=34,missing=33,gain=28.014679,cover=235.612625"
[556] "33:[f10<0.25999999] yes=49,no=50,missing=49,gain=8.73455811,cover=169.717682"
[557] "49:[f22<0.344999999] yes=67,no=68,missing=67,gain=1.84100342,cover=168.47934"
[558] "67:[f11<5.19000006] yes=81,no=82,missing=81,gain=1.10580444,cover=167.240158"
[559] "81:[f55<10.5] yes=95,no=96,missing=95,gain=0.38369751,cover=165.533585"
[560] "95:leaf=-0.0717453137,cover=153.215729"
[561] "96:leaf=-0.0499908663,cover=12.3178444"
[562] "82:leaf=-0.0177970473,cover=1.70657718"
[563] "68:leaf=-0.00623460393,cover=1.23919165"
[564] "50:leaf=0.0357006788,cover=1.23833072"
[565] "34:[f36<0.194999993] yes=51,no=52,missing=51,gain=13.361208,cover=65.8949509"
[566] "51:[f44<0.275000006] yes=69,no=70,missing=69,gain=10.4722939,cover=50.0305328"
[567] "69:[f26<0.0399999991] yes=83,no=84,missing=83,gain=12.0243769,cover=41.7472649"
[568] "83:[f20<0.610000014] yes=97,no=98,missing=97,gain=8.87806702,cover=35.4452209"
[569] "97:leaf=-0.0143138254,cover=22.8751507"
[570] "98:leaf=0.033934854,cover=12.5700703"
[571] "84:leaf=-0.0637696683,cover=6.30204582"
[572] "70:leaf=-0.0643068328,cover=8.28326702"
[573] "52:[f36<1.83999991] yes=71,no=72,missing=71,gain=5.16641617,cover=15.8644152"
[574] "71:leaf=-0.0777504146,cover=13.8940105"
[575] "72:leaf=0.00288101938,cover=1.97040486"
[576] "24:[f56<2038.5] yes=35,no=36,missing=35,gain=0.300720215,cover=114.290695"
[577] "35:[f22<0.105000004] yes=53,no=54,missing=53,gain=0.156311035,cover=113.080528"
[578] "53:leaf=-0.0791282207,cover=111.869255"
[579] "54:leaf=-0.0243669432,cover=1.21127391"
[580] "36:leaf=-0.0228758082,cover=1.21016335"
[581] "16:[f24<0.49000001] yes=25,no=26,missing=25,gain=8.55020523,cover=12.4734573"
[582] "25:[f49<0.0815000013] yes=37,no=38,missing=37,gain=4.95852947,cover=10.746542"
[583] "37:leaf=0.0619333498,cover=6.82739305"
[584] "38:[f56<110] yes=55,no=56,missing=55,gain=4.61223602,cover=3.9191494"
[585] "55:leaf=-0.0519720949,cover=1.48026979"
[586] "56:[f12<0.0700000003] yes=73,no=74,missing=73,gain=0.372016072,cover=2.43887949"
[587] "73:leaf=0.00621121051,cover=1.22187507"
[588] "74:leaf=0.0453526191,cover=1.21700454"
[589] "26:leaf=-0.054831557,cover=1.72691464"
[590] "8:[f26<0.38499999] yes=17,no=18,missing=17,gain=13.7725687,cover=43.9128494"
[591] "17:[f41<0.125] yes=27,no=28,missing=27,gain=7.54208279,cover=40.2327156"
[592] "27:[f44<0.605000019] yes=39,no=40,missing=39,gain=5.93320274,cover=38.4949303"
[593] "39:[f56<79.5] yes=57,no=58,missing=57,gain=4.67481422,cover=33.8062363"
[594] "57:[f15<1.28999996] yes=75,no=76,missing=75,gain=7.37915134,cover=12.3660164"
[595] "75:[f56<57.5] yes=85,no=86,missing=85,gain=1.14114237,cover=5.20718002"
[596] "85:leaf=-0.0504332297,cover=2.47129965"
[597] "86:[f55<11.5] yes=99,no=100,missing=99,gain=1.39369559,cover=2.73588061"
[598] "99:leaf=0.0195412915,cover=1.49050164"
[599] "100:leaf=-0.0324635878,cover=1.24537909"
[600] "76:[f55<4.5] yes=87,no=88,missing=87,gain=1.70775843,cover=7.15883589"
[601] "87:leaf=-0.000949524168,cover=1.9930861"
[602] "88:[f55<14.5] yes=101,no=102,missing=101,gain=0.870436668,cover=5.16575003"
[603] "101:leaf=0.0584340319,cover=3.93255448"
[604] "102:leaf=0.00701635377,cover=1.23319542"
[605] "58:[f24<0.400000006] yes=77,no=78,missing=77,gain=4.71152496,cover=21.440218"
[606] "77:[f17<0.194999993] yes=89,no=90,missing=89,gain=3.401474,cover=20.1990261"
[607] "89:[f1<0.219999999] yes=103,no=104,missing=103,gain=2.11026573,cover=12.3842335"
[608] "103:leaf=0.0715068579,cover=11.1628094"
[609] "104:leaf=0.00314736296,cover=1.22142494"
[610] "90:[f1<0.0700000003] yes=105,no=106,missing=105,gain=5.5844512,cover=7.81479263"
[611] "105:leaf=-0.0225393586,cover=3.21028066"
[612] "106:leaf=0.0512508154,cover=4.60451174"
[613] "78:leaf=-0.0284308251,cover=1.24119198"
[614] "40:[f15<1.07500005] yes=59,no=60,missing=59,gain=8.03460217,cover=4.68869591"
[615] "59:leaf=-0.0655433908,cover=2.73028636"
[616] "60:leaf=0.0407004356,cover=1.95840931"
[617] "28:leaf=-0.0577685796,cover=1.73778701"
[618] "18:leaf=-0.0663715824,cover=3.68013287"
[619] "4:[f26<0.140000001] yes=9,no=10,missing=9,gain=19.4047546,cover=40.7618523"
[620] "9:[f0<0.694999993] yes=19,no=20,missing=19,gain=1.99549103,cover=38.5551796"
[621] "19:[f20<0.254999995] yes=29,no=30,missing=29,gain=0.72102356,cover=37.3301926"
[622] "29:[f6<0.550000012] yes=41,no=42,missing=41,gain=2.31702709,cover=6.63244343"
[623] "41:leaf=-0.00663308334,cover=1.47511232"
[624] "42:[f11<0.754999995] yes=61,no=62,missing=61,gain=0.761068344,cover=5.15733099"
[625] "61:leaf=0.0660578683,cover=3.67545581"
[626] "62:leaf=0.0139469169,cover=1.48187542"
[627] "30:[f17<1.755] yes=43,no=44,missing=43,gain=0.131408691,cover=30.6977501"
[628] "43:leaf=0.0767412707,cover=29.4796562"
[629] "44:leaf=0.0237014443,cover=1.21809328"
[630] "20:leaf=0.00489485078,cover=1.22498512"
[631] "10:leaf=-0.0577083044,cover=2.20667553"
[632] "2:[f24<0.38499999] yes=5,no=6,missing=5,gain=69.3749237,cover=150.69458"
[633] "5:[f41<0.189999998] yes=11,no=12,missing=11,gain=15.082428,cover=139.513489"
[634] "11:[f26<0.210000008] yes=21,no=22,missing=21,gain=11.4065857,cover=137.543793"
[635] "21:[f36<0.454999983] yes=31,no=32,missing=31,gain=8.5880127,cover=136.312454"
[636] "31:[f24<0.199999988] yes=45,no=46,missing=45,gain=3.04562378,cover=132.645355"
[637] "45:[f55<5.5] yes=63,no=64,missing=63,gain=2.45614624,cover=129.930283"
[638] "63:leaf=0.0101295337,cover=1.73856366"
[639] "64:[f49<0.430000007] yes=79,no=80,missing=79,gain=1.27734375,cover=128.191711"
[640] "79:[f52<0.0535000004] yes=91,no=92,missing=91,gain=0.544403076,cover=125.481659"
[641] "91:[f0<0.0250000004] yes=107,no=108,missing=107,gain=2.57379532,cover=3.64250541"
[642] "107:leaf=0.0582112484,cover=2.43113232"
[643] "108:leaf=-0.0136027066,cover=1.21137297"
[644] "92:[f36<0.199999988] yes=109,no=110,missing=109,gain=0.527496338,cover=121.839157"
[645] "109:leaf=0.0760292262,cover=119.417488"
[646] "110:leaf=0.0307103693,cover=2.42166734"
[647] "80:[f52<0.732500017] yes=93,no=94,missing=93,gain=0.983843446,cover=2.71004987"
[648] "93:leaf=-0.00206113001,cover=1.48693752"
[649] "94:leaf=0.0466710888,cover=1.22311234"
[650] "46:[f9<0.360000014] yes=65,no=66,missing=65,gain=3.42512298,cover=2.71508551"
[651] "65:leaf=-0.0312154442,cover=1.23612535"
[652] "66:leaf=0.0510176942,cover=1.47896004"
[653] "32:[f36<0.75] yes=47,no=48,missing=47,gain=6.0947876,cover=3.66708994"
[654] "47:leaf=-0.0571650565,cover=1.48389125"
[655] "48:leaf=0.0423690751,cover=2.18319869"
[656] "22:leaf=-0.0479660891,cover=1.23134851"
[657] "12:leaf=-0.046671927,cover=1.96969104"
[658] "6:[f17<0.254999995] yes=13,no=14,missing=13,gain=8.95355511,cover=11.1810837"
[659] "13:leaf=-0.0691801235,cover=9.68907833"
[660] "14:leaf=0.0372856148,cover=1.49200487"
[661] "booster[6]"
[662] "0:[f52<0.0555000007] yes=1,no=2,missing=1,gain=461.852234,cover=582.450256"
[663] "1:[f20<0.435000002] yes=3,no=4,missing=3,gain=111.799133,cover=440.133331"
[664] "3:[f24<0.0949999988] yes=7,no=8,missing=7,gain=15.0053406,cover=288.071106"
[665] "7:[f55<9.5] yes=15,no=16,missing=15,gain=46.0451965,cover=198.795807"
[666] "15:[f56<2.5] yes=25,no=26,missing=25,gain=1.61482239,cover=124.911278"
[667] "25:[f18<2.26999998] yes=41,no=42,missing=41,gain=0.263286769,cover=2.65873456"
[668] "41:leaf=-0.00122348138,cover=1.20919847"
[669] "42:leaf=-0.0294536818,cover=1.44953609"
[670] "26:[f10<0.0700000003] yes=43,no=44,missing=43,gain=1.48132324,cover=122.252541"
[671] "43:[f18<3.03999996] yes=59,no=60,missing=59,gain=1.41073608,cover=121.037254"
[672] "59:leaf=-0.0720714033,cover=101.455635"
[673] "60:[f18<3.53999996] yes=75,no=76,missing=75,gain=1.81777763,cover=19.581625"
[674] "75:[f56<20.5] yes=89,no=90,missing=89,gain=2.80739045,cover=2.86226082"
[675] "89:leaf=-0.0452846512,cover=1.41826761"
[676] "90:leaf=0.0276161525,cover=1.44399321"
[677] "76:[f2<1.25] yes=91,no=92,missing=91,gain=0.20151329,cover=16.7193642"
[678] "91:leaf=-0.0592002533,cover=14.7995272"
[679] "92:leaf=-0.0217084214,cover=1.91983747"
[680] "44:leaf=-0.00834482536,cover=1.21528292"
[681] "16:[f45<0.0450000018] yes=27,no=28,missing=27,gain=27.239872,cover=73.8845367"
[682] "27:[f26<0.0350000001] yes=45,no=46,missing=45,gain=19.1153278,cover=52.9502945"
[683] "45:[f11<0.709999979] yes=61,no=62,missing=61,gain=10.5509844,cover=45.8022385"
[684] "61:[f36<0.0799999982] yes=77,no=78,missing=77,gain=5.25414181,cover=36.32547"
[685] "77:[f56<71.5] yes=93,no=94,missing=93,gain=8.20835495,cover=31.4230328"
[686] "93:[f18<3.2750001] yes=111,no=112,missing=111,gain=3.18053722,cover=11.6808128"
[687] "111:leaf=-0.0177167952,cover=8.4847517"
[688] "112:leaf=0.0320965871,cover=3.19606185"
[689] "94:[f48<1.91949999] yes=113,no=114,missing=113,gain=8.4648304,cover=19.7422199"
[690] "113:leaf=0.055491969,cover=18.5080013"
[691] "114:leaf=-0.0476060249,cover=1.23421717"
[692] "78:[f55<23] yes=95,no=96,missing=95,gain=3.96888041,cover=4.90243864"
[693] "95:leaf=0.0219918508,cover=2.1923871"
[694] "96:leaf=-0.0518925898,cover=2.71005154"
[695] "62:[f18<3.42499995] yes=79,no=80,missing=79,gain=8.36524391,cover=9.47676563"
[696] "79:[f55<33.5] yes=97,no=98,missing=97,gain=1.26965904,cover=7.24805403"
[697] "97:leaf=-0.0673491731,cover=5.06231403"
[698] "98:leaf=-0.0154570518,cover=2.18574023"
[699] "80:leaf=0.0371733904,cover=2.22871161"
[700] "46:leaf=-0.0687128827,cover=7.14805794"
[701] "28:[f27<0.229999989] yes=47,no=48,missing=47,gain=11.2468948,cover=20.9342403"
[702] "47:[f49<0.328500003] yes=63,no=64,missing=63,gain=0.0760574341,cover=19.4649239"
[703] "63:leaf=-0.0761391073,cover=18.2478027"
[704] "64:leaf=-0.0239169467,cover=1.21712136"
[705] "48:leaf=0.0414845794,cover=1.46931708"
[706] "8:[f52<0.0490000024] yes=17,no=18,missing=17,gain=0.423919678,cover=89.2752914"
[707] "17:[f56<1792.5] yes=29,no=30,missing=29,gain=0.122283936,cover=88.0932999"
[708] "29:leaf=-0.075566873,cover=86.8850021"
[709] "30:leaf=-0.0234189481,cover=1.20829725"
[710] "18:leaf=-0.0200113989,cover=1.18199039"
[711] "4:[f24<0.0649999976] yes=9,no=10,missing=9,gain=72.5565109,cover=152.06221"
[712] "9:[f56<74.5] yes=19,no=20,missing=19,gain=73.1253662,cover=117.971832"
[713] "19:[f16<0.185000002] yes=31,no=32,missing=31,gain=12.094574,cover=47.7575645"
[714] "31:[f55<4.5] yes=49,no=50,missing=49,gain=6.32559586,cover=44.7866058"
[715] "49:[f0<0.125] yes=65,no=66,missing=65,gain=1.94550133,cover=16.2244263"
[716] "65:leaf=-0.0687904507,cover=15.0145378"
[717] "66:leaf=-0.00329905143,cover=1.20988834"
[718] "50:[f44<0.545000017] yes=67,no=68,missing=67,gain=5.10538864,cover=28.5621815"
[719] "67:[f49<0.152999997] yes=81,no=82,missing=81,gain=5.65535545,cover=22.7968349"
[720] "81:[f55<27.5] yes=99,no=100,missing=99,gain=6.48450708,cover=16.9763374"
[721] "99:[f45<0.135000005] yes=115,no=116,missing=115,gain=3.27987528,cover=14.7622051"
[722] "115:leaf=0.020233117,cover=13.5684919"
[723] "116:leaf=-0.0428605527,cover=1.19371319"
[724] "100:leaf=-0.0614782572,cover=2.21413279"
[725] "82:[f11<1.53999996] yes=101,no=102,missing=101,gain=1.09513855,cover=5.82049704"
[726] "101:leaf=-0.0601873472,cover=4.59361696"
[727] "102:leaf=-0.00548951747,cover=1.22688043"
[728] "68:leaf=-0.0627191514,cover=5.76534557"
[729] "32:[f55<7.5] yes=51,no=52,missing=51,gain=0.660462141,cover=2.97095871"
[730] "51:leaf=0.0611179173,cover=1.74092197"
[731] "52:leaf=0.00935018063,cover=1.23003685"
[732] "20:[f45<0.0450000018] yes=33,no=34,missing=33,gain=41.822567,cover=70.2142715"
[733] "33:[f26<0.125] yes=53,no=54,missing=53,gain=36.8534088,cover=62.8999634"
[734] "53:[f49<0.20449999] yes=69,no=70,missing=69,gain=3.0344696,cover=58.3182755"
[735] "69:[f36<0.234999999] yes=83,no=84,missing=83,gain=1.30479431,cover=50.3271675"
[736] "83:[f50<0.0544999987] yes=103,no=104,missing=103,gain=1.0178833,cover=47.897934"
[737] "103:[f18<0.36500001] yes=117,no=118,missing=117,gain=1.17485046,cover=46.4633255"
[738] "117:leaf=0.0261897389,cover=2.66398335"
[739] "118:leaf=0.0738978609,cover=43.7993431"
[740] "104:leaf=0.0161122121,cover=1.43460822"
[741] "84:[f18<2.59500003] yes=105,no=106,missing=105,gain=0.0241444707,cover=2.42923069"
[742] "105:leaf=0.0266947187,cover=1.21768856"
[743] "106:leaf=0.00711577712,cover=1.21154213"
[744] "70:[f49<0.244499996] yes=85,no=86,missing=85,gain=8.03845978,cover=7.9911108"
[745] "85:leaf=-0.0437763184,cover=1.71649826"
[746] "86:[f9<0.319999993] yes=107,no=108,missing=107,gain=1.10936069,cover=6.27461243"
[747] "107:leaf=0.0663834587,cover=4.32530546"
[748] "108:leaf=0.0144368429,cover=1.9493072"
[749] "54:leaf=-0.0661141798,cover=4.5816865"
[750] "34:[f5<0.180000007] yes=55,no=56,missing=55,gain=0.675657272,cover=7.31430626"
[751] "55:leaf=-0.0679318085,cover=5.86892843"
[752] "56:leaf=-0.0161305536,cover=1.44537783"
[753] "10:[f10<0.425000012] yes=21,no=22,missing=21,gain=8.94389343,cover=34.0903816"
[754] "21:[f56<24.5] yes=35,no=36,missing=35,gain=0.133842468,cover=32.8594093"
[755] "35:leaf=-0.0223314986,cover=1.19420338"
[756] "36:leaf=-0.0735699981,cover=31.6652069"
[757] "22:leaf=0.0362558365,cover=1.23097014"
[758] "2:[f24<0.430000007] yes=5,no=6,missing=5,gain=61.312561,cover=142.31694"
[759] "5:[f55<4.5] yes=11,no=12,missing=11,gain=13.5397949,cover=133.420456"
[760] "11:leaf=-0.0418634303,cover=1.97210765"
[761] "12:[f26<0.0799999982] yes=23,no=24,missing=23,gain=10.5710144,cover=131.448349"
[762] "23:[f45<0.474999994] yes=37,no=38,missing=37,gain=4.70928955,cover=128.759308"
[763] "37:[f49<0.426999986] yes=57,no=58,missing=57,gain=2.34204102,cover=127.528252"
[764] "57:[f55<9.5] yes=71,no=72,missing=71,gain=0.292388916,cover=124.350342"
[765] "71:leaf=0.0375326499,cover=3.88394499"
[766] "72:[f3<0.199999988] yes=87,no=88,missing=87,gain=0.234710693,cover=120.466393"
[767] "87:[f45<0.109999999] yes=109,no=110,missing=109,gain=0.0672912598,cover=119.272041"
[768] "109:[f36<0.50999999] yes=119,no=120,missing=119,gain=0.0268554688,cover=118.066307"
[769] "119:leaf=0.0738880336,cover=115.172241"
[770] "120:leaf=0.0360830538,cover=2.89406347"
[771] "110:leaf=0.0231877677,cover=1.20573342"
[772] "88:leaf=0.0212451126,cover=1.19435513"
[773] "58:[f52<0.674000025] yes=73,no=74,missing=73,gain=3.90728831,cover=3.17791414"
[774] "73:leaf=-0.0310567543,cover=1.2407248"
[775] "74:leaf=0.0541597717,cover=1.93718934"
[776] "38:leaf=-0.0133692054,cover=1.2310499"
[777] "24:[f5<0.379999995] yes=39,no=40,missing=39,gain=3.07505798,cover=2.68905091"
[778] "39:leaf=-0.0523957647,cover=1.47890472"
[779] "40:leaf=0.0264709033,cover=1.21014619"
[780] "6:[f9<0.125] yes=13,no=14,missing=13,gain=2.60735703,cover=8.8964777"
[781] "13:leaf=-0.0694952756,cover=7.18155289"
[782] "14:leaf=-0.00305546215,cover=1.71492481"
[783] "booster[7]"
[784] "0:[f52<0.0394999981] yes=1,no=2,missing=1,gain=458.950745,cover=565.94043"
[785] "1:[f6<0.0649999976] yes=3,no=4,missing=3,gain=179.287598,cover=415.374207"
[786] "3:[f51<0.508000016] yes=7,no=8,missing=7,gain=120.804077,cover=377.670349"
[787] "7:[f23<0.0199999996] yes=15,no=16,missing=15,gain=24.8132324,cover=342.647156"
[788] "15:[f4<0.935000002] yes=27,no=28,missing=27,gain=10.6122437,cover=332.790405"
[789] "27:[f21<0.135000005] yes=39,no=40,missing=39,gain=9.87158203,cover=307.772858"
[790] "39:[f22<0.36500001] yes=55,no=56,missing=55,gain=3.48791504,cover=302.13092"
[791] "55:[f10<0.425000012] yes=75,no=76,missing=75,gain=3.20721436,cover=300.909424"
[792] "75:[f55<199.5] yes=85,no=86,missing=85,gain=2.97369385,cover=297.510437"
[793] "85:[f11<5.5] yes=91,no=92,missing=91,gain=2.17462158,cover=295.805481"
[794] "91:leaf=-0.0670703426,cover=294.594727"
[795] "92:leaf=-0.00169101206,cover=1.2107501"
[796] "86:leaf=-0.00233191974,cover=1.70494914"
[797] "76:[f55<11] yes=87,no=88,missing=87,gain=3.41122007,cover=3.39899111"
[798] "87:leaf=0.0335525312,cover=1.23531771"
[799] "88:leaf=-0.0441656485,cover=2.1636734"
[800] "56:leaf=0.00845336262,cover=1.22149134"
[801] "40:[f48<0.932500005] yes=57,no=58,missing=57,gain=7.71796608,cover=5.64193773"
[802] "57:[f49<0.0320000015] yes=77,no=78,missing=77,gain=0.475108862,cover=3.4358747"
[803] "77:leaf=0.0527803116,cover=1.70752263"
[804] "78:leaf=0.0113600139,cover=1.72835195"
[805] "58:leaf=-0.0577790365,cover=2.20606279"
[806] "28:[f18<1.86500001] yes=41,no=42,missing=41,gain=13.7350264,cover=25.0175533"
[807] "41:[f55<40.5] yes=59,no=60,missing=59,gain=1.24494743,cover=14.6033468"
[808] "59:[f55<2.5] yes=79,no=80,missing=79,gain=0.972473145,cover=13.4192629"
[809] "79:leaf=-0.0177991167,cover=1.87908173"
[810] "80:leaf=-0.0678472668,cover=11.5401812"
[811] "60:leaf=-0.00605169358,cover=1.18408334"
[812] "42:[f26<0.125] yes=61,no=62,missing=61,gain=8.34436321,cover=10.4142075"
[813] "61:[f24<0.200000003] yes=81,no=82,missing=81,gain=3.12621021,cover=8.05661583"
[814] "81:[f11<0.789999962] yes=89,no=90,missing=89,gain=1.49829531,cover=6.86358023"
[815] "89:leaf=0.0555539243,cover=5.40566349"
[816] "90:leaf=0.0012149103,cover=1.45791709"
[817] "82:leaf=-0.0232212525,cover=1.19303548"
[818] "62:leaf=-0.0541508049,cover=2.35759163"
[819] "16:[f55<12] yes=29,no=30,missing=29,gain=5.89489079,cover=9.85675144"
[820] "29:leaf=-0.0434130765,cover=2.1850071"
[821] "30:[f24<0.0850000009] yes=43,no=44,missing=43,gain=6.69099379,cover=7.67174482"
[822] "43:[f18<2.19000006] yes=63,no=64,missing=63,gain=0.886001587,cover=6.45435858"
[823] "63:leaf=0.0591343753,cover=5.235497"
[824] "64:leaf=0.00748740276,cover=1.2188617"
[825] "44:leaf=-0.0454784743,cover=1.21738601"
[826] "8:[f55<9.5] yes=17,no=18,missing=17,gain=21.9893112,cover=35.0231819"
[827] "17:[f51<1.48099995] yes=31,no=32,missing=31,gain=8.72223186,cover=13.6414328"
[828] "31:[f55<7.5] yes=45,no=46,missing=45,gain=2.66779709,cover=9.25445652"
[829] "45:[f51<1.12800002] yes=65,no=66,missing=65,gain=1.63433933,cover=7.08943129"
[830] "65:leaf=-0.064088881,cover=5.16709042"
[831] "66:leaf=-0.00858293939,cover=1.92234111"
[832] "46:leaf=0.00530613633,cover=2.16502523"
[833] "32:[f44<0.239999995] yes=47,no=48,missing=47,gain=1.37061763,cover=4.38697577"
[834] "47:[f55<4.5] yes=67,no=68,missing=67,gain=0.333889961,cover=3.17729068"
[835] "67:leaf=0.0176496524,cover=1.70354366"
[836] "68:leaf=0.0632316247,cover=1.47374713"
[837] "48:leaf=-0.00498855393,cover=1.20968509"
[838] "18:[f11<1.21000004] yes=33,no=34,missing=33,gain=2.14160156,cover=21.3817482"
[839] "33:[f44<0.924999952] yes=49,no=50,missing=49,gain=1.19697571,cover=18.741436"
[840] "49:[f17<1.39499998] yes=69,no=70,missing=69,gain=0.18195343,cover=17.5197124"
[841] "69:leaf=0.0722628087,cover=16.327364"
[842] "70:leaf=0.0211851411,cover=1.19234991"
[843] "50:leaf=0.0107760411,cover=1.22172225"
[844] "34:[f4<0.115000002] yes=51,no=52,missing=51,gain=1.72183871,cover=2.64031339"
[845] "51:leaf=-0.0165633075,cover=1.45538318"
[846] "52:leaf=0.0427634865,cover=1.18493009"
[847] "4:[f26<0.140000001] yes=9,no=10,missing=9,gain=14.7285156,cover=37.7038651"
[848] "9:[f0<0.694999993] yes=19,no=20,missing=19,gain=1.70128632,cover=35.7825928"
[849] "19:[f32<0.210000008] yes=35,no=36,missing=35,gain=0.904159546,cover=34.565464"
[850] "35:leaf=0.0675885528,cover=32.8635674"
[851] "36:leaf=0.0176198836,cover=1.70189524"
[852] "20:leaf=0.00448365603,cover=1.21712899"
[853] "10:leaf=-0.0523889288,cover=1.92127085"
[854] "2:[f24<0.38499999] yes=5,no=6,missing=5,gain=73.6363525,cover=150.566238"
[855] "5:[f55<6.5] yes=11,no=12,missing=11,gain=9.93634033,cover=138.219177"
[856] "11:[f18<2.84000015] yes=21,no=22,missing=21,gain=6.15424824,cover=3.93204546"
[857] "21:leaf=-0.0472475961,cover=2.45528889"
[858] "22:leaf=0.0512636416,cover=1.47675657"
[859] "12:[f25<0.155000001] yes=23,no=24,missing=23,gain=6.98129272,cover=134.28714"
[860] "23:[f51<0.0445000008] yes=37,no=38,missing=37,gain=1.55847168,cover=132.321915"
[861] "37:[f52<0.145999998] yes=53,no=54,missing=53,gain=3.78569031,cover=20.6449013"
[862] "53:[f55<27] yes=71,no=72,missing=71,gain=5.76138926,cover=6.47535992"
[863] "71:[f52<0.0944999978] yes=83,no=84,missing=83,gain=0.707058311,cover=2.90828133"
[864] "83:leaf=-0.000728548621,cover=1.42903948"
[865] "84:leaf=-0.0433053859,cover=1.47924197"
[866] "72:leaf=0.0515949167,cover=3.56707835"
[867] "54:[f49<0.290499985] yes=73,no=74,missing=73,gain=1.26310158,cover=14.1695404"
[868] "73:leaf=0.0680549294,cover=12.9345617"
[869] "74:leaf=0.00911991391,cover=1.23497891"
[870] "38:leaf=0.0719276965,cover=111.67701"
[871] "24:leaf=-0.014810198,cover=1.9652276"
[872] "6:[f17<0.254999995] yes=13,no=14,missing=13,gain=6.15266228,cover=12.3470507"
[873] "13:[f10<0.0949999988] yes=25,no=26,missing=25,gain=0.608272552,cover=11.1078005"
[874] "25:leaf=-0.0697837546,cover=8.98122692"
[875] "26:leaf=-0.0234400928,cover=2.12657404"
[876] "14:leaf=0.0299481079,cover=1.23924971"
[877] "booster[8]"
[878] "0:[f6<0.00999999978] yes=1,no=2,missing=1,gain=315.267731,cover=556.894043"
[879] "1:[f15<0.13499999] yes=3,no=4,missing=3,gain=202.438934,cover=462.731018"
[880] "3:[f54<3.3829999] yes=7,no=8,missing=7,gain=85.954071,cover=375.032257"
[881] "7:[f18<2.16499996] yes=13,no=14,missing=13,gain=17.2284851,cover=306.116425"
[882] "13:[f56<117.5] yes=23,no=24,missing=23,gain=5.5753479,cover=228.78479"
[883] "23:[f20<2.61000013] yes=37,no=38,missing=37,gain=4.06747437,cover=164.63504"
[884] "37:[f56<2.5] yes=49,no=50,missing=49,gain=1.63769531,cover=160.870636"
[885] "49:leaf=-0.0135285193,cover=1.66574466"
[886] "50:[f4<5.75500011] yes=69,no=70,missing=69,gain=0.48928833,cover=159.20488"
[887] "69:[f16<1.51999998] yes=89,no=90,missing=89,gain=0.211364746,cover=158.055313"
[888] "89:leaf=-0.0714667067,cover=156.886368"
[889] "90:leaf=-0.0206716172,cover=1.16895199"
[890] "70:leaf=-0.0175416972,cover=1.14957011"
[891] "38:[f56<31.5] yes=51,no=52,missing=51,gain=3.08192563,cover=3.76440501"
[892] "51:leaf=-0.0484975092,cover=1.84813321"
[893] "52:leaf=0.0221214127,cover=1.91627192"
[894] "24:[f24<0.0649999976] yes=39,no=40,missing=39,gain=20.3050308,cover=64.1497574"
[895] "39:[f4<0.0949999988] yes=53,no=54,missing=53,gain=10.175559,cover=20.2081337"
[896] "53:[f18<0.0149999997] yes=71,no=72,missing=71,gain=4.92045498,cover=10.4371986"
[897] "71:[f56<213.5] yes=91,no=92,missing=91,gain=1.15698385,cover=3.12494636"
[898] "91:leaf=0.0258036461,cover=1.9412787"
[899] "92:leaf=-0.0203562453,cover=1.18366754"
[900] "72:leaf=-0.0602691695,cover=7.31225252"
[901] "54:[f54<2.2105] yes=73,no=74,missing=73,gain=5.16204739,cover=9.7709341"
[902] "73:[f20<0.00999999978] yes=93,no=94,missing=93,gain=2.44471717,cover=4.49862623"
[903] "93:leaf=-0.0467628315,cover=1.6261096"
[904] "94:[f4<0.330000013] yes=111,no=112,missing=111,gain=1.21911085,cover=2.87251639"
[905] "111:leaf=-0.0137980906,cover=1.42718136"
[906] "112:leaf=0.0350723155,cover=1.44533503"
[907] "74:[f5<0.0649999976] yes=95,no=96,missing=95,gain=0.910862446,cover=5.27230787"
[908] "95:leaf=0.0606687889,cover=4.09253168"
[909] "96:leaf=0.00713712908,cover=1.17977619"
[910] "40:[f56<1689] yes=55,no=56,missing=55,gain=0.292999268,cover=43.9416237"
[911] "55:[f5<0.305000007] yes=75,no=76,missing=75,gain=0.0782852173,cover=42.7748375"
[912] "75:leaf=-0.069887504,cover=41.6187553"
[913] "76:leaf=-0.0213018917,cover=1.15608358"
[914] "56:leaf=-0.0188320335,cover=1.16678596"
[915] "14:[f16<0.0599999987] yes=25,no=26,missing=25,gain=28.7163315,cover=77.3316345"
[916] "25:[f54<2.23099995] yes=41,no=42,missing=41,gain=11.0889969,cover=69.3026581"
[917] "41:[f4<1.53499997] yes=57,no=58,missing=57,gain=2.05787659,cover=52.4674873"
[918] "57:[f20<5.32000017] yes=77,no=78,missing=77,gain=1.39201355,cover=50.5402641"
[919] "77:[f55<20] yes=97,no=98,missing=97,gain=0.178581238,cover=49.1534309"
[920] "97:leaf=-0.0617618412,cover=47.7469254"
[921] "98:leaf=-0.0197356082,cover=1.40650582"
[922] "78:leaf=-0.00696035009,cover=1.38683605"
[923] "58:leaf=-0.00608289475,cover=1.92722201"
[924] "42:[f45<0.194999993] yes=59,no=60,missing=59,gain=8.30294991,cover=16.8351746"
[925] "59:[f24<0.140000001] yes=79,no=80,missing=79,gain=6.8429842,cover=12.4197741"
[926] "79:[f55<10.5] yes=99,no=100,missing=99,gain=2.67717981,cover=10.1055555"
[927] "99:[f54<2.5710001] yes=113,no=114,missing=113,gain=1.11979461,cover=2.86090207"
[928] "113:leaf=-0.0299129244,cover=1.64323306"
[929] "114:leaf=0.0168077145,cover=1.21766901"
[930] "100:[f54<2.96150017] yes=115,no=116,missing=115,gain=2.08602238,cover=7.24465418"
[931] "115:leaf=0.0497148111,cover=6.03021479"
[932] "116:leaf=-0.0105939731,cover=1.21443939"
[933] "80:leaf=-0.0525308065,cover=2.31421781"
[934] "60:leaf=-0.0619255304,cover=4.41540003"
[935] "26:[f24<0.330000013] yes=43,no=44,missing=43,gain=6.83088875,cover=8.02896976"
[936] "43:[f18<2.6400001] yes=61,no=62,missing=61,gain=1.26396465,cover=6.87990952"
[937] "61:leaf=0.00799700525,cover=1.17723596"
[938] "62:leaf=0.069382675,cover=5.70267391"
[939] "44:leaf=-0.039787434,cover=1.14906037"
[940] "8:[f24<0.399999976] yes=15,no=16,missing=15,gain=43.8071785,cover=68.9158173"
[941] "15:[f26<0.0799999982] yes=27,no=28,missing=27,gain=26.7945442,cover=52.9330215"
[942] "27:[f45<0.289999992] yes=45,no=46,missing=45,gain=20.9054928,cover=46.1636238"
[943] "45:[f48<0.965499997] yes=63,no=64,missing=63,gain=11.0330772,cover=42.5718422"
[944] "63:[f20<5.83500004] yes=81,no=82,missing=81,gain=5.01498413,cover=40.8601227"
[945] "81:[f27<0.254999995] yes=101,no=102,missing=101,gain=4.44314575,cover=39.6626091"
[946] "101:[f56<39.5] yes=117,no=118,missing=117,gain=2.8016243,cover=38.467308"
[947] "117:leaf=-0.00232150988,cover=1.7029016"
[948] "118:leaf=0.0587980635,cover=36.7644043"
[949] "102:leaf=-0.0224786438,cover=1.19530082"
[950] "82:leaf=-0.0282265265,cover=1.19751668"
[951] "64:leaf=-0.0527230352,cover=1.71171951"
[952] "46:leaf=-0.0632199794,cover=3.59177995"
[953] "28:leaf=-0.0604201667,cover=6.76939774"
[954] "16:[f1<0.224999994] yes=29,no=30,missing=29,gain=1.53117371,cover=15.9827976"
[955] "29:leaf=-0.0699146613,cover=14.5395679"
[956] "30:leaf=-0.0102449078,cover=1.44322956"
[957] "4:[f24<0.24000001] yes=9,no=10,missing=9,gain=26.1059761,cover=87.6987762"
[958] "9:[f45<0.0949999988] yes=17,no=18,missing=17,gain=26.9176674,cover=80.319191"
[959] "17:[f26<0.125] yes=31,no=32,missing=31,gain=17.8476334,cover=73.700737"
[960] "31:[f54<1.91149998] yes=47,no=48,missing=47,gain=5.95172119,cover=71.0730362"
[961] "47:[f54<1.76999998] yes=65,no=66,missing=65,gain=4.12049198,cover=11.63661"
[962] "65:[f56<59.5] yes=83,no=84,missing=83,gain=4.1490345,cover=10.1757908"
[963] "83:[f18<4.76999998] yes=103,no=104,missing=103,gain=2.74155498,cover=3.91365051"
[964] "103:[f56<37] yes=119,no=120,missing=119,gain=0.439514399,cover=2.43754983"
[965] "119:leaf=-0.00515461247,cover=1.22476411"
[966] "120:leaf=-0.0457686,cover=1.21278572"
[967] "104:leaf=0.0330946892,cover=1.47610068"
[968] "84:[f49<0.0384999998] yes=105,no=106,missing=105,gain=1.27940273,cover=6.2621398"
[969] "105:leaf=0.00435280893,cover=1.20229101"
[970] "106:leaf=0.06206901,cover=5.05984879"
[971] "66:leaf=-0.0377444178,cover=1.46081948"
[972] "48:[f4<0.0250000004] yes=67,no=68,missing=67,gain=1.2751236,cover=59.4364281"
[973] "67:[f55<51] yes=85,no=86,missing=85,gain=5.51075172,cover=22.5306702"
[974] "85:[f9<0.544999957] yes=107,no=108,missing=107,gain=0.601593018,cover=14.2857647"
[975] "107:leaf=0.067780748,cover=12.375598"
[976] "108:leaf=0.0214266218,cover=1.91016686"
[977] "86:[f55<71.5] yes=109,no=110,missing=109,gain=9.29779339,cover=8.24490547"
[978] "109:[f9<0.200000003] yes=121,no=122,missing=121,gain=3.60089111,cover=3.63291621"
[979] "121:leaf=-0.0636051521,cover=2.47579312"
[980] "122:leaf=0.0202959646,cover=1.15712297"
[981] "110:leaf=0.0532125644,cover=4.61198902"
[982] "68:[f55<11.5] yes=87,no=88,missing=87,gain=2.54815674,cover=36.9057579"
[983] "87:leaf=0.00614168262,cover=1.67877948"
[984] "88:leaf=0.0701231509,cover=35.2269783"
[985] "32:leaf=-0.0572026931,cover=2.62769866"
[986] "18:[f18<0.25999999] yes=33,no=34,missing=33,gain=5.00948572,cover=6.61845732"
[987] "33:leaf=0.013304254,cover=1.99062049"
[988] "34:leaf=-0.0689263716,cover=4.6278367"
[989] "10:[f15<0.695000052] yes=19,no=20,missing=19,gain=3.63807869,cover=7.37958431"
[990] "19:leaf=-0.0652228072,cover=5.42344952"
[991] "20:leaf=0.00608874299,cover=1.9561348"
[992] "2:[f26<0.0799999982] yes=5,no=6,missing=5,gain=18.7544556,cover=94.1629944"
[993] "5:[f45<0.174999997] yes=11,no=12,missing=11,gain=6.9163208,cover=92.0004883"
[994] "11:[f24<0.189999998] yes=21,no=22,missing=21,gain=4.12678528,cover=90.3084946"
[995] "21:[f49<0.431500018] yes=35,no=36,missing=35,gain=2.34806824,cover=88.1045685"
[996] "35:leaf=0.0712989718,cover=86.9008179"
[997] "36:leaf=0.00233534025,cover=1.20375609"
[998] "22:leaf=0.0030164218,cover=2.20392513"
[999] "12:leaf=-0.0189272892,cover=1.69199574"
[1000] "6:leaf=-0.0563426651,cover=2.16250682"
[ reached getOption("max.print") -- omitted 2666 entries ]xgb.plot.tree(model=xgb_final)Evaluation
Recommend using model. Risk of overfitting real, though holdout wants you to believe otherwise. Should try SHAP for detailed review… later
Final model against holdout
xgb_pred_finalvsholdout<-predict(xgb_final,holdout_data)
library(pROC)
my_roc_finalvsholdout<-roc(holdout_label,xgb_pred_finalvsholdout)Setting levels: control = 0, case = 1
Setting direction: controls < casescoords(my_roc_finalvsholdout,"best",ret=c("threshold","specificity","sensitivity","accuracy",
"precision","recall"),transpose=FALSE)plot.roc(holdout_label,xgb_pred_finalvsholdout)Setting levels: control = 0, case = 1
Setting direction: controls < cases
plot(my_roc_finalvsholdout,print.thres="best",
print.thres.best.weights=c(50,0.2))
Confusion matrix
xgb_pred <- ifelse (xgb_pred_finalvsholdout>=0.3290529,1,0)
table(holdout_label, xgb_pred) xgb_pred
holdout_label 0 1
0 249 16
1 4 187Deployment
XGBoost recommended. Need modern tool for email scraping, since this is party like it’s 1999 stuff. Otherwise make pipeline and deploy. Spam goes to quarantine non spam goes to your inbox. Bribe SAM to install with pizza at Proto’s.
---
title: "Spam Notebook"
output: html_notebook
---
# Business Understanding
Spam can lead to some bad stuff, let's stop it.

## Load libraries and packages
```{r}
library(e1071)  
require(xgboost)
library(pROC)
library(magrittr)
library(dplyr)
```
Load data 
```{r}
rm(list=ls())
spam<-read.csv("/Users/kent/Library/Mobile Documents/com~apple~CloudDocs/Leeds/Spring/MSBX5415-002AdvancedDataAnalytics/Project/dataset_44_spambase.csv",header=T,na.strings=c("","NA"))
```
Quick look at top of the file
```{r}
head(spam)
```
Quick look at the bottom of the file
```{r}
tail(spam)
```
How big is file
```{r}
dim(spam)
```
# Data Understanding
This data is a curated sample from HP, should not be considered a random sample, each row is an email
word_freq... = frequency of word in email
char_freq... = " character
capital... = stats on capitalized...
class = label, target, binary

Look at content, quick summary
```{r}
str(spam)
```
Descriptive stats
```{r}
summary(spam)
```

```{r}
names<-names(spam)
```
Look at column headers
```{r}
print(names)
```

```{r}
column_names<-colnames(spam)
```
Look at target, labeled 1 as spam, 0 as not spam
```{r}
as.factor(spam$class)
```
Look at amount of zero's, data seems to be super clean and ready to roll, 0's are legitimate since not every email has dictionary in it, content wise
```{r}
colSums(is.na(spam))
for (i in names(spam)){
  print(c('Percentage of zeros in ',i,sum(spam[,i]==0)/nrow(spam)))}
```
## A little EDA

Look at skewness
```{r}
for (i in names(spam)){
  print(c('Skewness in ',i,skewness(spam[,i])))}

for (i in names(spam)){
  hist(spam[,i],main=i,breaks=50)}
```
## A little more EDA

Look at boxplots
```{r}
for (i in names(spam)){
  boxplot(spam[,i],main=i)}
```
## A even a little EDA

Look at correlations
```{r}
M <- cor(spam)
library(corrplot)
corrplot(M,method="color",order="FPC")
```
# Data Preparation

Convert the class factor to an integer class starting at 0.
This is picky, but it's a requirement for XGBoost
```{r}
class<-spam$class
label<-as.integer(spam$class)
spam$class<-NULL
```
Split the data for training and testing (70/20/10 train/test/holdout split)
```{r}
n<-nrow(spam)                                
set.seed(123)
train_index<-sample(n,floor(0.70*n))         
train_data<-as.matrix(spam[train_index,])
train_label<-label[train_index]

testholdout_data<-spam[-train_index,]       
testholdout_label<-label[-train_index]
nth<-nrow(testholdout_data)
th_index<-sample(nth,floor(0.67*nth))        
test_data<-as.matrix(testholdout_data[th_index,])
test_label<-testholdout_label[th_index]
holdout_data<-as.matrix(testholdout_data[-th_index,])
holdout_label<-testholdout_label[-th_index]
```
Transform the two data sets into xgb.Matrix
```{r}
xgb_train<-xgb.DMatrix(data=train_data,label=train_label)
xgb_test<-xgb.DMatrix(data=test_data,label=test_label)
```
Get the number of negative & positive cases in our data
```{r}
negative_cases <- sum(train_label==0)
postive_cases <- sum(train_label==1)
```
# Modeling
## XGBoost

Chosen because I am trying to use a different model for every project

First pass
```{r}
params<-list(booster="gbtree",                       # default
             objective="binary:logistic",            # the objective function
             eta=0.3,                                
             gamma=0,
             max_depth=6,                            # the maximum depth of each decision tree
             min_child_weight=1,
#             scale_pos_weight = negative_cases/postive_cases,   # NOT DEFAULT control for imbalanced classes
             subsample=1,
             colsample_bytree=1)
set.seed(123)
xgbcv<-xgb.cv(params=params,
              data=xgb_train,
              nrounds=100,                        
              nfold=5,
 #             metrics = list("rmse","auc"),
              showsd=T,
              stratified=T,
              print_every_n=10,
              early_stopping_rounds=10,            
              maximize=F)
```
Review model
```{r}
xgbcv
min(xgbcv$evaluation_log$test_logloss_mean)
```
Train the XGBoost classifer
```{r}
set.seed(123)
xgb1<-xgb.train(params=params,
                data=xgb_train,                    
                nrounds=42,
                watchlist=list(val=xgb_test,train=xgb_train),
                print_every_n=10,
                early_stopping_rounds=10,
                maximize=F,
                eval_metric="error")                  # 'logloss'
```
Predict outcomes with the test data
```{r}
xgb1_pred<-predict(xgb1,test_data)
my_roc1<-roc(test_label,xgb1_pred)
plot(my_roc1,print.thres="best",print.thres.best.method="youden",
     print.thres.best.weights=c(50,0.2))
coords(my_roc1,"best",ret=c("threshold","specificity","sensitivity","accuracy",
                           "precision","recall"),transpose=FALSE)
```

```{r}
importance_matrix1<-xgb.importance(model=xgb1)
print(importance_matrix1)
xgb.plot.importance(importance_matrix=importance_matrix1)
xgb.dump(xgb1,with_stats=TRUE)
xgb.plot.tree(model=xgb1)
```
Define hyperparameter grid, really big! = really slow
```{r}
ntrees<-42
hyper_grid<-expand.grid(
  eta=2/ntrees,
  max_depth=c(3,4,5,6,8,10),
  min_child_weight=c(1,2,3),
  subsample=c(0.5,0.75,1),
  colsample_bytree=c(0.4,0.6,0.8,1),
  gamma=c(0,1,3,10,100,1000),
  error=0,          # a place to dump error results
  trees=0)          # a place to dump required number of trees
```
Grid search
```{r}
for(i in seq_len(nrow(hyper_grid))) {
  set.seed(123)
  m<-xgb.cv(
    data=train_data,
    label=train_label,
    nrounds=ntrees,
    objective="binary:logistic",
    early_stopping_rounds=10,
    nfold=5,
    verbose=1,
    eval_metric="error",
    params=list(
      eta=hyper_grid$eta[i],
      max_depth=hyper_grid$max_depth[i],
      min_child_weight=hyper_grid$min_child_weight[i],
      subsample=hyper_grid$subsample[i],
      colsample_bytree=hyper_grid$colsample_bytree[i],
      gamma=hyper_grid$gamma[i])
  )
  #hyper_grid$logloss[i]<-min(m$evaluation_log$test_logloss_mean)
  hyper_grid$error[i]<-min(m$evaluation_log$test_error_mean)
  hyper_grid$trees[i]<-m$best_iteration}
```
Results of search
```{r}
hyper_grid %>%
  filter(error>0) %>%
  arrange(error) %>%
  glimpse()
```
Optimal parameter list from results of grid search
```{r}
params<-list(
  eta=0.04761905,
  max_depth=10,
  min_child_weight=1,
  subsample=0.75,
  colsample_bytree=0.6,
  gamma=0)
```
Train final model
```{r}
set.seed(123)
xgb_final<-xgboost(
  params=params,
  data=train_data,
  label=train_label,
  nrounds=31,
  objective="binary:logistic",
  early_stopping_rounds=10,
  nfold=5,
  verbose = 0,
  eval_metric="error")

xgb_pred_final<-predict(xgb_final,test_data)
library(pROC)
my_roc_final<-roc(test_label,xgb_pred_final)
coords(my_roc_final,"best",ret=c("threshold","specificity","sensitivity","accuracy",
                           "precision","recall"),transpose=FALSE)
```
Importance matrix
```{r}
importance_matrix<-xgb.importance(model=xgb_final)
print(importance_matrix)
xgb.plot.importance(importance_matrix=importance_matrix)
xgb.dump(xgb_final,with_stats=TRUE)
xgb.plot.tree(model=xgb_final)
```
# Evaluation

Recommend using model. Risk of overfitting real, though holdout wants you to believe otherwise.  Should try SHAP for detailed review... later

Final model against holdout
```{r}
xgb_pred_finalvsholdout<-predict(xgb_final,holdout_data)
library(pROC)
my_roc_finalvsholdout<-roc(holdout_label,xgb_pred_finalvsholdout)
coords(my_roc_finalvsholdout,"best",ret=c("threshold","specificity","sensitivity","accuracy",
                                 "precision","recall"),transpose=FALSE)
```

```{r}
plot.roc(holdout_label,xgb_pred_finalvsholdout)
plot(my_roc_finalvsholdout,print.thres="best",       
     print.thres.best.weights=c(50,0.2))
```
Confusion matrix
```{r}
xgb_pred <- ifelse (xgb_pred_finalvsholdout>=0.3290529,1,0)
table(holdout_label, xgb_pred)  
```
# Deployment

XGBoost recommended. Need modern tool for email scraping, since this is party like it's 1999 stuff. Otherwise make pipeline and deploy. Spam goes to quarantine non spam goes to your inbox.  Bribe SAM to install with pizza at Proto's.




